# A Million Tweets Are Worth a Few Points:
Tuning Transformers for Customer Service Tasks

Amir Hadifar† Sofie Labat‡ Véronique Hoste‡
Chris Develder† Thomas Demeester†
† IDLab, Dept. of Information Technology, Ghent University - imec, Belgium
‡ LT3, Dept. of Translation, Interpreting and Communication, Ghent University,
Belgium

###### Abstract

In online domain-specific customer service applications, many companies
struggle to deploy advanced NLP models successfully, due to the limited
availability of and noise in their datasets. While prior research demonstrated
the potential of migrating large open-domain pretrained models for domain-
specific tasks, the appropriate (pre)training strategies have not yet been
rigorously evaluated in such social media customer service settings,
especially under multilingual conditions. We address this gap by  (i)
collecting a multilingual social media corpus containing customer service
conversations (865k tweets),  (ii) comparing various pipelines of pretraining
and finetuning approaches,  (iii) applying them on 5 different end tasks.  We
show that pretraining a generic multilingual transformer model on our in-
domain dataset, before finetuning on specific end tasks, consistently boosts
performance, especially in non-English settings.111Dataset and code available
at https://github.com/hadifar/customerservicetasks

##  1 Introduction

Online platforms and social media are increasingly important as communication
channels in various companies’ customer relationship management (CRM). To
ensure effective, qualitative and timely customer service, Natural Language
Processing (NLP) can assist by providing insights to optimize customer
interactions, but also in real-time tasks:  (i) detect emotions Gupta et al.
(2010),  (ii) categorize or prioritize customer tickets Molino et al. (2018),
(iii) aid in virtual assistants through natural language understanding and/or
generation Cui et al. (2017), etc.

Despite this NLP progress for CRM, often small and medium-sized companies
(SMEs) struggle with applying such recent technology due to the limited size,
noise and imbalance in their datasets. General solutions to such challenges
are transfer learning strategies Ruder (2019): _feature extraction_ uses
frozen model parameters after pretraining on an external (larger) training
corpus, while _finetuning_ continues training on the smaller in-domain corpus.
In the large body of work adopting such strategies (e.g., Pan and Yang 2009),
little effort has been put into addressing specific CRM use cases that need to
rely on social media data that is noisy, possibly multilingual, and domain-
specific for a given company.

In this paper, we analyze the possibilities and limitations of transfer
learning for a number of CRM tasks, following up on the findings of Gururangan
et al. (2020) who demonstrate gains from progressive finetuning on in-domain
and task-specific monolingual data. Specifically, our contributions are that
we  (1) collect a multilingual corpus of 275k Twitter conversations,
comprising 865k tweets between customers and companies in 4 languages (EN, FR,
DE, NL), covering distinct sectors (telecom, public transport, airline)
(Section 4.1);  (2) rigorously compare combinations of pretraining and
finetuning strategies (Section 3) on 5 different CRM tasks (Section 4.2),
including prediction of complaints, churn, subjectivity, relevance, and
polarity; and  (3) provide empirical results (Section 5).  We find that
additional pretraining on a moderately sized in-domain corpus, before task-
specific finetuning, contributes to overcoming the lack of a large
multilingual domain-specific language model. Its effect is much stronger than
consecutive finetuning on smaller datasets for related end tasks. Furthermore,
our experimental results show that when pretrained models are used in feature
extraction mode, they struggle to beat well-tuned classical baselines.

##  2 Related Work

A wide range of NLP research has been devoted to customer services. Hui and
Jha (2000) employed data mining techniques to extract features from a customer
service database for decision support and machine fault diagnosis. Gupta
(2011) extracted a set of sentiment and syntactic features from tweets for
customer problem identification tasks. Molino et al. (2018) introduced the
Customer Obsession Ticket Assistant for ticket resolution, using feature
engineering techniques and encoder-decoder models.

Highly popular pretrained language models, such as BERT Devlin et al. (2019),
have also been explored for different customer service tasks: Hardalov et al.
(2019) considered re-ranking candidate answers in chatbots, while Deng et al.
(2020) proposed BERT-based topic prediction for incoming customer requests.
Although the performance gains obtained by pretraining language models are
well-established, they need further exploration in terms of multilinguality.
Some studies Pires et al. (2019); Karthikeyan et al. (2019); Wu et al. (2019)
have investigated the transferability of multilingual models on different
tasks, but they do not consider the effect of progressive pretraining on a
smaller and less diverse multilingual corpus, as we will do.

##  3 Methodology

###  3.1 Architecture

We selected some of the most popular publicly available pretrained language
models to explore transfer learning properties for CRM classification tasks:
RoBERTa Liu et al. (2019), XLM Conneau et al. (2020), and BERTweet Nguyen et
al. (2020). These models are pretrained on the English Wikipedia and
BookCorpus Zhu et al. (2015), CommonCrawl in 100 languages, and 850M English
tweets, respectively. The XLM and BERTweet pretraining procedure is based on
RoBERTa, which itself is a transformer-based Masked Language Model (MLM;
Devlin et al., 2019). All of these models require a different classifier
‘head’ for each target task to estimate the probability of a class label.

###  3.2 Transfer Strategies

We adopt a straightforward approach to transfer learned representations: we
continue pretraining the considered transformer models on a 4-lingual corpus
of customer service Twitter conversations (see Section 4.1), i.e., the overall
domain of all considered sub-tasks. After that, we apply additional adaptation
for cross-lingual transfer (Section 5.1), as well as cross-task transfer
(Section 5.2).

The following notations are used throughout the rest of this paper to describe
pretraining stages:

  * •

$\pi$ – further pretraining the original MLM on our 4-lingual tweet
conversation corpus.

  * •

$\varphi$ – finetuning the pretrained model extended with the MLP classifier
on the target task

  * •

$\varnothing$ – freezing the pretrained model (i.e., feature extraction mode),
only training the top classifier on the target task.

We thus indicate several multistage procedures: e.g.,
XLM$\rightarrow{\pi}\rightarrow{\varphi}$ indicates that the XLM model is
further pretrained on the in-domain tweet corpus, followed by finetuning on
the end task.

##  4 Experimental Setup

We focus our experiments on text classification problems that are commonly
dealt with by customer service teams. First, we describe our Twitter
conversation corpus used for in-domain finetuning (Section 4.1), then we
introduce the target tasks and corresponding datasets (Section 4.2). For most
target tasks, we hold out 10% of the data for testing, while the remaining
part is used for training. We then utilize 10-fold cross-validation on the
training data to select optimal hyper-parameters for each end task. When the
dataset comes with a predefined train-test split, we keep that. For the
pretrained transformer models (RoBERTa, XLM, BERTweet), we use the publicly
available ‘base’ versions.

###  4.1 Twitter Conversation Corpus

Our corpus for in-domain pretraining was crawled using Twitter’s
API.222https://developer.twitter.com/en/docs The collected dataset is small
compared to the original language models’ data, but still larger than most
corpora which SMEs have at their disposal. As such, it represents an easily
collectable customer service dataset that SMEs can leverage to boost models on
their own data.

The tweets were gathered between May and October 2020. We started by gathering
a list of 104 companies, all active on Twitter, in the sectors of
telecommunication, public transportation, and airlines. We aimed for four
different languages (English, French, Dutch, German).

We preprocessed the data by removing conversations not covering at least one
client/company interaction, or containing undefined languages. We further
converted usernames and links into the special tokens @USER and @HTTP_URL,
respectively, and converted emojis into corresponding strings.333We used
https://github.com/carpedm20/emoji to convert emojis The resulting corpus
contains 865k tweets over 275k conversations in the four target languages (see
Table 1).

Even though our corpus contains data from different sectors, we noticed that
the dialogue flow, customer intents, and structure of conversations are fairly
comparable across the target sectors (cf. Fig. 1). Examples of often recurring
types of tweets are expressions of gratitude towards customers, requests for
information, or typical ways to reply to complaints. Hence, we expect this
corpus to be useful not only for companies that fall under one of the included
sectors, but also for other companies that provide customer sewrvices over
tweets.

Language | $|\text{convs}|$ | $|\text{tweets}|$
---|---|---
English | 135.1k | 406.3k
French | 60.9k | 212.9k
Dutch | 45.6k | 141.0k
German | 33.0k | 104.5k
All | 274.6k | 864.7k
Table 1:  Number of collected conversations ($|\text{convs}|$) and tweets ($|\text{tweets}|$) for each language.  Figure 1: 2D visualization of the first hidden-state representations of the XLM on randomly sampled tweets for six companies, in three different sectors using t-SNE Van der Maaten and Hinton (2008). Each color stands for a different sector, while the marker types represent the different companies, and each data point corresponds to a single operator tweet (covering multiple languages).  | Complaint-2 | Complaint-R | Churn | Subjectivity | Relevance | Polarity
---|---|---|---|---|---|---
| (English) | (French) | (English) | (Dutch) | (German) | (German)
Model | ACC | F1 | ACC | F1 | ACC | F1 | ACC | F1 | F1syn. | F1dia. | F1syn. | F1dia.
Majority-class | 72.4 | 42.0 | 56.0 | 35.8 | 78.4 | 43.9 | 55.0 | 35.5 | 81.6 | 83.9 | 65.6 | 67.2
LR (tf-idf) | 83.5 | 77.0 | 57.5 | 57.4 | 85.1 | 71.7 | 71.6 | 70.9 | 88.4 | 87.7 | 71.1 | 70.4
SVM (tf-idf) | 84.4 | 80.2 | 59.0 | 58.8 | 87.3 | 80.1 | 71.7 | 71.0 | 90.4 | 88.8 | 74.8 | 72.8
Reference | 82.0[1] | 62.7[1] | - | - | - | 78.3[2] | - | - | 85.2[3] | 86.8[3] | 66.7[3] | 69.4[3]
BERTweet $\rightarrow$ $\varnothing$ | 80.5 | 71.6 | - | - | 79.3 | 55.2 | - | - | - | - | - | -
BERTweet $\rightarrow$ $\varphi$ | 90.0 | 86.1 | - | - | 93.0 | 90.0 | - | - | - | - | - | -
RoBERTa $\rightarrow$ $\varnothing$ | 77.9 | 74.5 | - | - | 78.3 | 59.7 | - | - | - | - | - | -
RoBERTa $\rightarrow$ $\varphi$ | 87.5 | 85.1 | - | - | 88.4 | 84.8 | - | - | - | - | - | -
XLM $\rightarrow$ $\varnothing$ | 76.2 | 61.6 | 44.0 | 30.5 | 61.6 | 55.7 | 63.8 | 62.4 | 83.1 | 84.7 | 64.5 | 66.8
XLM $\rightarrow$ $\varphi$ | 85.4 | 83.4 | 54.0 | 46.2 | 84.1 | 75.3 | 73.4 | 72.9 | 91.6 | 91.7 | 76.1 | 73.5
XLM $\rightarrow$ $\pi$$\rightarrow$ $\varnothing$ | 81.8 | 76.8 | 56.5 | 54.1 | 79.7 | 66.0 | 71.6 | 71.1 | 84.4 | 85.3 | 65.1 | 68.0
XLM $\rightarrow$ $\pi$$\rightarrow$ $\varphi$ | 86.9 | 82.7 | 62.0 | 61.9 | 87.8 | 83.7 | 74.6 | 74.2 | 92.7 | 92.5 | 78.7 | 76.1
Table 2:  Classification results (accuracy ACC and F1-score) on CRM tasks
using pretrained language models with two settings for pretraining: Feature
extraction ($\rightarrow$ $\varnothing$) and finetuning ($\rightarrow$
$\varphi$). Missing values (‘-’) are due to unavailable reference scores, or a
language mismatch between model and task.
[1] Greenleaf et al. (2015), [2] Amiri and Daume III (2015), [3] Wojatzki et
al. (2017)

###  4.2 CRM Tasks and Datasets

Complaint Prediction – Timely complaint detection is of utmost importance to
organizations, as it can improve their relationship with customers and prevent
customer churns. Preoţiuc-Pietro et al. (2019) and Greenleaf et al. (2015)
proposed two datasets for identifying complaints on social media which contain
3,499 and 5,143 instances, respectively. The former (Complaint-2) covers two
types of companies (airline companies and telecommunication), while the latter
(Complaint-9) consists of data from nine domains such as food, car, software,
etc.

Both datasets are in English. To experiment with cross-lingual tuning for
complaint prediction, we use the French complaint dataset for railway
companies from (Complaint-R; Ruytenbeek et al. 2020). Since all their 201
conversations are labeled as complaints, for training, we complemented them
with negative sampling from French railway conversations in our own Twitter
corpus. For testing, we annotated 200 held-out conversations.

Churn prediction – Customer churn implies that a customer stops using a
company’s service, negatively impacting its growth. Churn prediction is cast
as a binary classification task (churn or non-churn) on any input text. We
utilize the data provided by Amiri and Daume III (2015) with tweets from three
telecommunication brands, resulting in a corpus of 4,339 labelled English
tweets.

Subjectivity Prediction – Detecting subjectivity in conversations is a key
task for companies to efficiently address negative customer feelings or reward
loyal customers. It may also serve as a filtering task for more fine-grained
tasks such as emotion identification. We annotated 8,174 Dutch conversations
from our Twitter corpus (Section 4.1). A dialogue is judged “subjective” if at
least one of the customer turns contains emotions (explicit or implicit), and
otherwise “objective”.

Relevance Prediction – The goal of this task is to determine whether an
incoming text is relevant for further processing or not.

We use data from GermEval 2017 (Task A) which contains over 28k short length
messages from various social media and web sources on the German public train
operator Deutsche Bahn Wojatzki et al. (2017). For this dataset, the
evaluation is measured on two evaluation sets: one collected from the same
time period as the training and development set (viz. synchronic), and another
one containing data from a later time period (viz. diachronic).

Polarity Prediction – For this task, a system has to classify the sentiment
that resides in a given text fragment according to polarity (positive,
negative, or neutral). Polarity prediction has often been applied on reviews,
by predicting the attitude or sentiment of a reviewer with respect to some
topic. We use the GermEval 2017 (Task B) dataset Wojatzki et al. (2017) (cf.
supra) to analyze the polarity of the Deutsche Bahn customers’ feedback. We
also use the polarity dataset from Sanders (2011) (Sanders).

##  5 Results and Discussion

We now present our findings for two finetuning scenarios: transfer across
languages and across tasks. Section 5.1 investigates the effect of
unsupervised multilingual pretraining. Section 5.2 then explores how to
further improve by finetuning the pretrained language models on similar tasks.

###  5.1 Language Transferability

We compare the pretrained transformer experiments with the following
baselines: majority-class (to get an idea of class imbalance), logistic
regression (LR) and support vector machine (SVM) with tf-idf features. For the
three transformer models, we compare the feature extraction setting
($\rightarrow$ $\varnothing$) with finetuning ($\rightarrow$ $\varphi$) on the
target task. On the multilingual XLM, we measure the impact of first
pretraining ($\rightarrow$ $\pi$) on our multilingual tweet corpus, after
which both transfer settings are again tested on the target tasks. Table 2
reports the results (in terms of accuracy and F1 scores), including scores
from literature when available (‘Reference’). It should be noted that the
reference scores are not state-of-the-art, but they are the scores
communicated in the original dataset papers.

Only for the English tasks (Complaint-2 and Churn), results for BERTweet and
RoBERTa are reported. The monolingual tweet-based model BERTweet outperforms
all other models when finetuned on these tasks. Although a large domain-
specific mono-lingual language model seems a fine choice, it may not be
available for other languages. We therefore investigate the impact of a
multilingual generic model (XLM was not specifically pretrained on tweets),
and the impact of additional finetuning on our dedicated twitter corpus.

In general, transformer models finetuned on the end task strongly outperform
frozen ones. For the non-English tasks, the model XLM$\rightarrow$
$\varnothing$ with the frozen XLM encoder shows weak performance, in some
cases below the baselines. The model XLM$\rightarrow$ $\varphi$ finetuned on
the end task performs better. For the non-English tasks, the XLM model
pretrained on our Twitter corpus and finetuned on the tasks (XLM$\rightarrow$
$\pi$$\rightarrow$ $\varphi$) in all cases outperforms the finetuned XLM by a
few percentage points and the baselines by an even larger margin. The
performance differences between XLM$\rightarrow$ $\varphi$ and
XLM$\rightarrow$ $\pi$$\rightarrow$ $\varphi$ clearly underscore the
importance of in-domain multilingual pretraining. Furthermore, the results of
XLM$\rightarrow$ $\pi$$\rightarrow$ $\varphi$ for the English tasks suggest
that additional pretraining on a moderately small, in-domain dataset can make
the performance of the multilingual XLM model comparable to the monolingual
RoBERTa.

Another promising observation is that the hyper-tuned classical baselines,
such as SVM, are strong competitors compared to _frozen_ language models,
especially on tasks that are highly sensitive to domain-specific features. For
instance, for churn prediction, keywords such as ‘switch to’, ‘quit’ and
‘change provider’ can easily be triggered by the SVM, while _frozen_
pretrained models have not learned to identify these features. This finding
might be helpful to achieve better insight into the operational aspects of
_frozen_ neural models compared to simple classical approaches.

As a side result (not explicitly included in this work) we found that the
multistage pretraining (XLM$\rightarrow$ $\pi$) leads to better performance
when incorporating multiple languages compared to a single language. The
performance drops especially when training data from a single language (e.g.,
Dutch) is fed into the model, which is then evaluated on other languages
(e.g., English).

###  5.2 Task Transferability

We now investigate to what extent representations tuned on a related task can
help for a given target task. In particular, Complaint-9 is the end task, and
we compare the effect of finetuning on the end task only, vis-à-vis first
finetuning on a related task and then on the end task. For the related task,
we experiment with Complaint-2 and Sanders, as shown in Table 3. We observe
that there seems to be no clear merit in the additional finetuning step on a
small related end task. Pretraining on our larger Twitter corpus, however,
still increases effectiveness.

Test Dataset | Complaint-9
---|---
Train Dataset | C-9 | C-2 & C-9 | S & C-9
Majority-class | 39.1 | - | -
SVM (tf-idf) | 78.6 | - | -
Preotiuc-Pietro et al. | 79.0 | - | -
XLM $\rightarrow$ $\varphi$ | 78.6 | 79.3 | 80.1
XLM $\rightarrow$ $\pi$$\rightarrow$ $\varphi$ | 82.4 | 80.0 | 82.8
Table 3: F1 test scores on Complaint-9 for finetuning on the end task alone
(indicated as C-9) vs. on Complaint-2 or Sanders and again on Complaint-9
(columns C-2 & C-9, respectively, S & C-9.).

##  6 Conclusion

We investigated multilingual and across-task transfer learning for customer
support tasks, based on transformer-based language models. We confirmed prior
insights that finetuning the models on low-resource end tasks is important.
Additional pretraining on a moderately sized in-domain corpus, however,
provides a complementary increase in effectiveness, especially in the non-
English setting and starting from a generic multilingual language model. We
provide a newly collected multilingual in-domain corpus for customer service
tasks and derive the aforementioned findings from experiments using it on five
different tasks.

##  7 Acknowledgments

This research received funding from the Flemish Government under the Research
Program Artificial Intelligence - 174B09119. We would also like to thank the
anonymous reviewers for their valuable and constructive feedback.

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# The Nature of the IMBH Candidate CXO J133815.6+043255: High-Frequency Radio
Emission

Krista Lynne Smith George P. and Cynthia Woods Mitchell Institute for
Fundamental Physics and Astronomy, Texas A&M University, College Station, TX
77843-4242, USA  Southern Methodist University, Department of Physics Dallas,
TX 75205  Macon Magno  George P. and Cynthia Woods Mitchell Institute for
Fundamental Physics and Astronomy, Texas A&M University, College Station, TX
77843-4242, USA  Southern Methodist University, Department of Physics Dallas,
TX 75205  CSIRO Space and Astronomy, ATNF, PO Box 1130, Bentley WA 6102,
Australia  Ashutosh Tripathi  George P. and Cynthia Woods Mitchell Institute
for Fundamental Physics and Astronomy, Texas A&M University, College Station,
TX 77843-4242, USA  Southern Methodist University, Department of Physics
Dallas, TX 75205

###### Abstract

The ultra-luminous X-ray source CXO J133815.6+043255 is a strong candidate for
a bona-fide intermediate mass black hole, residing in the outskirts of NGC
5252. We present 22 GHz radio observations of this source obtained
serendipitously in an ongoing high-frequency imaging survey of radio-quiet
Active Galactic Nuclei (AGN), and use this new data point to construct the
broad-band radio spectral energy distribution (SED). We find that the SED
exhibits a spectral slope of $\alpha=-0.66\pm 0.02$, consistent with a steep
spectrum from optically-thin synchrotron emission from an unresolved jet. We
also find that the $L_{R}/L_{X}$ ratio is approximately $10^{-3}$,
inconsistent with radio-quiet AGN and many ULXs but consistent with low-
luminosity AGN (LLAGN) and radio-loud quasars. Together, these observations
support the conclusion that CXO J133815.6+043255 is an intermediate-mass black
hole producing a low-mass analog of radio jets seen in classical quasars.

Black holes (162) — Intermediate-mass black holes (816) — Active galactic
nuclei (16)

##  1 Introduction

Intermediate mass black holes (IMBHs) fall in the mass gap between stellar
remnants ($M_{\mathrm{BH}}\sim 10M_{\odot}$) and their supermassive
counterparts ($M_{\mathrm{BH}}\sim 10^{6}-10^{10}M_{\odot}$). Evidence for
black holes in the lower portion of this intermediate range is now commonplace
in gravitational wave detections from the Laser Interferometer Gravitational
Wave Observatory (LIGO), which routinely observes mergers of $\sim
10M_{\odot}$ black holes that presumably produce a remnant with
$M_{\mathrm{BH}}\sim 10^{2}M_{\odot}$ (e.g., Abbott et al., 2020).
Electromagnetic observations have long suggested the presence of black holes
in this mass range, although never unambiguously. Colbert & Mushotzky (1999)
first interpreted bright X-ray sources in spiral galaxies as possible black
holes with masses of $10^{2}-10^{4}~{}M_{\odot}$. In the past two
decades,observational focus on ultra-luminous X-ray sources (ULXs) has
generated a number of IMBH candidates. X-ray spectroscopy of ULXs has been
found to be consistent with the cooler accretion disks of smaller black holes,
although sometimes requiring the assumption of unusual accretion states
(Miller et al., 2003; Bachetti et al., 2013; Palit & Mondal, 2023).
Measurements of the radio and X-ray luminosities have allowed mass estimates
via the black hole fundamental plane (Merloni et al., 2003) that suggest
intermediate values for some objects (Mezcua & Lobanov, 2011; Cseh et al.,
2012). The object HLX-1, located in the galaxy ESO 243-49, has an X-ray
luminosity implying a strong lower bound of a few hundred solar masses that is
supported by numerous electromagnetic follow-up studies (Farrell et al.,
2010). X-ray timing has also delivered several candidates, from observations
of quasi-periodic oscillations in several ULXs that indicate intermediate
masses if typical frequency-mass scaling relations are followed (e.g., M 82
X-1, NGC 5408 X-1 Pasham et al., 2014; Strohmayer et al., 2007). While these
objects remain viable IMBH candidates, it has become clear that the majority
of ULXs are most likely to be particular accretion states of X-ray binaries,
especially neutron stars accreting at many times the Eddington rate, as
described in recent reviews (Fabrika et al., 2021; King et al., 2023).

One especially interesting IMBH candidate is CXO J133815.6+043255 (hereafter
CXO J1338+04), an ultra-luminous X-ray source (ULX) in the outskirts of the
lenticular Seyfert galaxy NGC 5252. This object has been carefully studied by
a handful of detailed investigations over the past decade. It was discovered
by Kim et al. (2015) in a search of archival _Chandra_ images, and confirmed
to have AGN-like optical ionization line ratios by the same study. In follow
up investigations, Kim et al. (2017) found evidence that the ionized gas in
the vicinity was kinematically bound to the object and has very low
metallicity, leading the authors to posit that the ULX was once the central
black hole in a dwarf galaxy that is now in the late stages of a minor merger
with NGC 5252. Mezcua et al. (2018) provided the strongest evidence of an IMBH
in the source so far: a VLBI image showing a resolved, 2-component radio
source at the location of the ULX, with an SED analysis suggesting that one
source is the radio core, and the other a small jet lobe. Using the black hole
fundamental plane (Merloni et al., 2003), they constrain the black hole mass
to $10^{3.5}M_{\odot}<M_{\mathrm{BH}}<10^{6.3}M_{\odot}$. Finally, Kim et al.
(2020) conducted near-infrared (NIR) imaging of the source with the _Herschel_
space telescope to study the stellar properties of the putative remnant dwarf
host, finding a stellar mass of $M_{*}=10^{7.9}M_{\odot}$ for the remnant
galaxy, and used scaling relations to estimate the IMBH mass at
$10^{5}M_{\odot}$.

Figure 1: (a) Close-up view of our K-band radio image of ULX CXO J1338+04.
Contours occur at 70, 50, 30, and 15% of the peak flux density. Inset is a
reproduction of the VLBI image from Mezcua et al. (2018); the very small black
box in the center, from which the inset is enlarged, has the dimensions of the
VLBI image. (b) _Left:_ Full K-band radio image of NGC 5252, including the ULX
in the top right. The beam is shown on bottom right. _Right:_ The PanSTARRS
g-band image of NGC 5252 with radio contours overlaid in red. The scalebar
shows the 10.3 kpc distance between the Seyfert nucleus and the ULX.

In this work, we present data obtained serendipitously for CXO J1338+04 in our
22 GHz radio survey of nearby radio-quiet ultra-hard X-ray selected AGN from
the _Swift_ -BAT survey conducted over the past several years with the Jansky
Very Large Array (JVLA) (Smith et al., 2016, 2020a).

The main target in our radio observation was NGC 5252, but our field of view
easily encompasses CXO J1338+04, which is robustly detected ($>5\sigma$). We
combine our new, high-frequency radio flux density with archival radio data,
and analyze the source’s X-ray to radio luminosity ratio in the context of
other accreting sources to place further constraints on its nature.

In Section 2, we discuss the JVLA observation and data reduction. In Section
3, we present the broadband radio spectral energy distribution and the
$L_{R}/L_{X}$ results. Section 4 provides some discussion of the results, and
a conclusive summary can be found in Section 5.

Throughout, we assume cosmological parameters $H_{0}=69.6$ km s-1 Mpc-1,
$\Omega_{M}=0.286$, and $\Omega_{\Lambda}=0.714$ (Bennett et al., 2014).

##  2 Observation and Data Reduction

The object CXO J1338+04 was observed in the same integration as its host
galaxy NGC 5252, as part of our JVLA 22 GHz imaging survey of radio-quiet AGN
host galaxies from the _Swift_ -BAT ultra-hard X-ray survey (Baumgartner et
al., 2013). The observations were taken in the K-band while the array was in
C-configuration, so the resulting images have a beam size of 1″. We have
presented the results of the first two phases of this survey in three papers
(Smith et al., 2016, 2020a, 2020b); it is the radio segment of the larger BAT
AGN Spectroscopic Survey (BASS; Koss et al., 2017, 2022), a multi-wavelength
follow-up effort for the full BAT AGN sample.

Our observations of NGC 5252 occurred on March 2, 2020 as part of an execution
block including two other science targets. The block began with X- and K-band
attenuation scans and an X-band reference pointing scan on 3C 286. The science
observation of NGC 5252 had an on-source integration time of 8 minutes and 50
seconds, and was bracketed on either side by gain calibration scans of
J1347+1217 and preceded by an X-band pointing calibration scan of this same
target. The raw data were passed through the standard JVLA reduction pipeline
at the National Radio Astronomy Observatory (NRAO). We then processed them
using the Common Astronomy Software Applications package (v. 4.5, McMullin et
al., 2007), and inspected the data for radio-frequency interference (RFI) or
bad phase calibration interactively with CASA’s plotms. In the case of NGC
5252, neither effect was found. Finally, each science target was split from
the main measurement set and averaged over all 64 channels within each
spectral window, and the data were cleaned to a 0.03 mJy threshold with Briggs
weighting.

The image obtained by this observation is shown in Figure 1 with an inset of
the VLBI image of Mezcua et al. (2018) and alongside the $g$-band image of NGC
5252 from the Pan-STARRS survey (Chambers et al., 2016). The ULX is
approximately 10 kpc from the galaxy center. The radio source is unresolved in
our image with $\theta_{\mathrm{maj}}=1.07$″ and
$\theta_{\mathrm{min}}=0.82$″; the resolution of our observations is much
larger than the VLBI image from Mezcua et al. (2018) in which the pc-scale jet
was resolved.

##  3 Results

###  3.1 Radio Spectral Energy Distribution

We measure an unresolved flux density of $0.53\pm{0.02}$ mJy for CXO J1338+04.
(The Seyfert nucleus in NGC 5252 was detected at $6.39\pm{0.04}$ mJy; this and
the core flux densities for our full sample will be published in the upcoming
final paper in our survey series, Magno et al. in preparation.)

Several observations at lower radio frequencies exist in the literature. These
are helpfully tabulated by Yang et al. (2017) in their Table 2. We note that
they noticed no significant variability in the radio flux density despite
these archival measurements occurring over several decades. Our beam size is
slightly smaller than the majority of the VLA beam sizes for observations
taken at 1.49 GHz ($\sim 1.5$″) and slightly larger than VLA beam sizes for
observations taken at 8.4 GHz ($\sim 0.3\arcsec$). We construct an SED with
these archival observations in Figure 2. The flux densities of 8.4 GHz
observations, taken annually from 1990 through 1993 (Wilson & Tsvetanov, 1994;
Kukula et al., 1995; Nagar et al., 1999), are all consistent with one another
and at similar resolution ($\sim 0.3$″, taken in A and A/B hybrid
configurations of the VLA), so we average these measurements in the plot and
fitting.

CXO J1338+04 is also robustly detected in the VLA Sky Survey (VLASS; Lacy et
al., 2020) at 3 GHz and 2.5″ resolution. We used an Epoch 1.2 VLASS image
centered on NGC 5252. This image was generated using the Canadian Initiative
for Radio Astronomy Data Analysis (CIRADA) cutout service111cutouts.cirada.ca.
Flux density was measured using the Cube Analysis and Rendering Tool for
Astronomy (CARTA; Comrie et al., 2021) interface: we create a 2.5″$\times$2.0″
region with a position angle of 18.45 degrees centered on the ULX, fully
containing its emission. CARTA calculates the flux density by multiplying the
mean intensity in this region by the number of beam areas subtended (where the
beam area is defined as the volume of the elliptical Gaussian defined by the
synthesized beam, divided by the maximum of that function). We obtain a VLASS
3 GHz flux density of $1.53\pm 0.16$ mJy; however, the currently available
VLASS images have a systematic error of 15% according to its documentation
(0.22 on our measured flux). We use this value in the plot and the spectral
slope fit.

There have also been a number of VLBI investigations of the source. Yang et
al. (2017) observed it with the EVN at 1.66 GHz and find it unresolved with a
flux density of $1.8\pm 0.1$ mJy. The VLBA observations of Mezcua et al.
(2018) resolved the radio source into two components along a roughly east-west
axis, with a combined total flux density of $0.66\pm 0.09$ mJy. The eastern
component is detected at both of their observing frequencies (4.4 GHz and 7.6
GHz), with a very steep spectral index between the two sources of
$\alpha=-2.0\pm 0.1$, where $S_{\nu}\propto\nu^{\alpha}$. Due to this well-
constrained, steep spectral index, Mezcua et al. (2018) believe the eastern
component to be a jet lobe, associated with the radio core in the western
component. The western component is detected at $0.12\pm 0.03$ mJy at 7.6 GHz,
and is not detected at 4.4 GHz. Based on the $5\sigma$ upper limit of the flux
density, they place an upper limit on the spectral index of this component of
$\alpha\sim-0.6$; this flatter index is most consistent with the western
source harboring the true radio core. The Yang et al. (2017) observation
likely failed to resolve the two components because of the unfortunate
coincidence that its beam is highly elongated along the apparent jet axis.
Because of the dramatically higher resolution of these VLBI observations, we
do not include them in the SED fitting, although they are relevant to the
upcoming $L_{R}/L_{X}$ discussion in Section 3.2.

Our 22 GHz data point extends the lower-resolution SED significantly in
frequency space. Using least-squares minimization, we calculate an overall
spectral index of $\alpha=-0.66\pm 0.02$ between 1.4 and 22 GHz, a typical
steep spectral index consistent with the majority of emission at these
resolutions coming from an unresolved jet source.

It is clear from the VLBI-scale observations of Mezcua et al. (2018) that a
jet-like radio source is present at the position of the ULX within our beam.
However, since the spatial resolution of our 22 GHz observation and many of
the archival observations shown in Figure 2 are not on VLBI scales, it is
possible that other sources such as star-forming regions are contributing to
the emission. Star-forming HII regions tend to have $\alpha\sim-0.1$ (Condon,
1992) due to significant contributions from bremsstrahlung radiation. Isolated
supernova remnants tend to have $\alpha<-0.6$ (e.g., Kapińska et al., 2017),
although the relatively flat thermal component of nearby HII regions typically
leads to a flatter composite spectrum of the star forming region as a whole.
X-ray binaries often have flat or inverted spectra (e.g., Migliari et al.,
2003). It is therefore unlikely, but not impossible, that a spectral index of
$\alpha\sim-0.6$ is consistent with star formation; however, Kim et al. (2015)
found AGN-like optical emission line ratios from long-slit spectroscopic
observations of CXO 1338+04 with $\sim 1\arcsec$ resolution, inconsistent with
ratios expected from HII regions. With all of this accounted for, it is
unlikely that star formation related sources are causing the steep radio
spectral index reported here.

###  3.2 The $L_{R}/L_{X}$ Ratio

Laor & Behar (2008) found that the relationship between the radio and X-ray
luminosities of radio-quiet Seyferts and quasars is the same as that exhibited
by cool, coronally active stars: $L_{R}/L_{X}\sim 10^{-5}$. In that work, the
authors compare radio-loud and radio-quiet optically-selected Palomar-Green
quasar subsamples (PG; Schmidt & Green, 1983) studied in detail by Boroson &
Green (1992) and four ULXs: NGC 5408 X-1 (Kaaret et al., 2003), M82 X-1
(Kaaret et al., 2006), NGC 7424 ULX-2 (Soria et al., 2006), and Holmberg II
(Dewangan et al., 2004). As described in detail in Laor & Behar (2008), the
radio fluxes of the PG sample were measured at 5 GHz by the VLA (Kellermann et
al., 1989, 1994) and the X-ray fluxes come from ROSAT observations by Brandt
et al. (2000) and Laor & Brandt (2002). The bolometric X-ray luminosity is
derived over the range 0.2-20 keV using the relation $L_{X}=C\nu
L_{\nu}(1\mathrm{keV})$, with $C=6.25$; for the detailed derivation, see
Section 2.1 of Laor & Behar (2008). The radio and X-ray luminosities of the
four ULXs come from diverse sources as detailed in Section 2.3 of Laor & Behar
(2008) and the references above, but are typically derived at 5 GHz and 2-10
keV. The radio-loud and radio-quiet quasars occupy distinctly different
populations in $L_{R}/L_{X}$ space, with the four ULXs consistent with the
radio-quiet population.

Figure 2: Spectral energy distribution of CXO J1338+04, including data
summarized by Yang et al. (2017), and our data point at 22 GHz (log
$\frac{\nu}{\mathrm{GHz}}=1.34$). Circles represent the relative sizes of the
beam for each observation; the smallest is the 0.36″ 1.6 GHz MERLIN
observations by Thean et al. (2001), and $\sim 0.3$″ resolutions of the
averaged 8.4 GHz observations by Wilson & Tsvetanov (1994), Kukula et al.
(1995), and Nagar et al. (1999). The largest is the 1.4 GHz flux observation
from the FIRST survey, at $\sim 6$″ (Becker et al., 1995). Figure 3: Ratio of
the radio and X-ray luminosities compared to the X-ray luminosity for the Laor
& Behar (2008) (LB08) quasar and ULX samples, the BAT AGN Seyfert sample from
Smith et al. (2020b), the LLAGN sample from Plotkin et al. (2012), and the ULX
being studied in this work, CXO J1338+04. The radio luminosities for the LB08
sample are at 5 GHz for the quasars and a variety of frequencies near 5 GHz
for the ULXs. The BAT AGN luminosities are taken from the 1″core at 22 GHz. We
therefore show the values for CXO J1338+04 measured at 22 GHz by our survey,
and at 4.4 GHz by Mezcua et al. (2018).

The Laor & Behar (2008) comparison also includes a small sample of low-
luminosity Seyferts; instead of that sample of 12, which required many
corrections of the X-ray and radio fluxes, we include two larger samples: the
LLAGN sample from Plotkin et al. (2012), and the sample of 100 nearby
($z<0.05$) Seyferts at 22 GHz from an earlier phase of the same survey in
which we detect CXO J1338+04 here, as well as the radio and X-ray lumionsities
of a further $\sim 150$ objects that represent the final phase of the survey
(and are also radio-quiet Seyferts). The survey will be published fully in an
upcoming work (Magno et al., in preparation). The large majority (96%) of the
22 GHz survey sources are radio-quiet, and are consistent with LLAGN along the
fundamental plane of black hole activity (Smith et al., 2020b).

The $0.3-8$ keV X-ray luminosity of CXO J1338+04 is $L_{X}=1.5\times 10^{40}$
erg s-1 (Kim et al., 2015). When compared to the 4.4 GHz radio luminosity
derived from the flux density of the total jet + core morphology from Mezcua
et al. (2018), this results in a ratio of $L_{R}/L_{X}=1.3\times 10^{-3}$. In
Figure 3, we plot this value along with the objects given in Laor & Behar
(2008), the LLAGN of Plotkin et al. (2012), and the Smith et al. (2020b) high-
frequency survey of radio-quiet Seyferts.

Our own observed flux results in a luminosity of $L_{\mathrm{22GHz}}=5.2\times
10^{37}$ erg s-1, which when compared to the X-ray luminosity yields
$L_{R}/L_{X}=4.3\times 10^{-3}$.

We note that the samples compared in this plot were taken from a diverse array
of literature sources, and were not all obtained at the same radio or X-ray
frequencies or energies. Canonically, the threshold between radio-loud and
radio-quiet AGN was proposed by Terashima & Wilson (2003) as $R_{X}\equiv\nu
L_{\nu,5\mathrm{GHz}}/L_{X,2-10\mathrm{keV}}\sim-4.5$. Laor & Behar (2008)
also use the radio luminosity at 5 GHz, but in X-rays use the integrated
$0.2-20$keV obtained with the conversion factor $C_{\nu}=6.25$ (see preceding
discussion in this section). Therefore, the X-ray luminosities of objects in
Figure3 may shift left or right by a factor of $\sim 5$, or 0.5 dex. The
effect of using different bands on the $L_{R}/L_{X}$ ratio can be visually
estimated by the spread between our 22 GHz data point and the corresponding
4.4 GHz data point from Mezcua et al. (2018); although their data point is
taken with VLBI at significantly smaller spatial scales, it remains above ours
by about 0.3 dex. All of the Smith et al. (2020b) points are taken at 22 GHz,
and remain mostly consistent in $L_{R}/L_{X}$ with the radio-quiet quasars at
5 GHz from Laor & Behar (2008) (despite being at lower X-ray luminosities, as
expected for Seyferts as compared to luminous quasars). We have used the
_Swift-XRT_ 2-10 keV X-ray luminosities (rather than the ultra-hard _Swift-
BAT_ luminosities) when computing the $L_{X}$ of the Smith et al. (2020a)
sample, for maximum consistency with the other samples. It is therefore
unlikely that the distinct populations on the plot are due to the observations
being taken at different bands or energies; and in any case, CXO 1338+04
retains its relative position to the other populations at both radio
frequencies and resolutions presented.

##  4 Discussion

When combined with archival observations, our high-frequency data point
extends the radio SED significantly. The best-fitting spectral index of
$\alpha=-0.66\pm 0.02$ is consistent with the canonical value for steep
spectrum radio emission from a synchrotron jet (e.g., Krolik, 1999). Because
the resolved VLBI observations of Mezcua et al. (2018) indicated a possible
core and jet-lobe morphology at much smaller scales, it is reasonable to
assume that the larger-scale unresolved radio emission probed by the archival
data and our new observation shown in Figure 2 is due to this same jet.

Radio-loud and radio-quiet samples show a clear dichotomy in their
$L_{R}/L_{X}$ ratios. The ratio between the radio and X-ray luminosity of CXO
J1338+04 is consistent with it being a low-mass analog of a radio-loud AGN or
quasar, as shown in Figure 3. The same conclusion was reached by Mezcua et al.
(2018) based on the 4.4 GHz flux of only the western component of their
resolved EVN image, which they believe is the location of the core. We have
added a point to Figure 3 that shows the ratio for the entire integrated 4.4
GHz flux from their observation, as well as for our unresolved 22 GHz
observation. Both points are far above the radio-quiet quasars from Laor &
Behar (2008) and from the 95% radio-quiet BAT AGN sample from Smith et al.
(2020b). In fact, the $L_{R}/L_{X}$ ratio is most consistent with radio-loud
quasars and the LLAGN sample from Plotkin et al. (2012), which was chosen to
be analogous to the “low-hard” accretion state of X-ray binaries. The origin
of radio emission in radio-loud objects is reasonably well-established as a
classical jet, and the LLAGN population is considered most likely to produce
jets in accordance with the disk-jet coupling model due to its position on the
same fundamental plane as low-hard X-ray binaries and the fact that most LLAGN
are radio-loud (Merloni et al., 2003; Falcke et al., 2004; Terashima & Wilson,
2003), this observation further supports the interpretation of the radio
emission from CXO J1338+04 as a jet launched by an accreting intermediate-mass
black hole. In this case, it joins the recently discovered jet candidate found
in the dwarf elliptical galaxy SDSS J090613.77+561015.2 (Yang et al., 2023),
which is likely to have a black hole mass of $3.6\times 10^{5}M_{\odot}$
(Baldassare et al., 2016).

It is also apparent from Figure 3 that CXO J1338+04 is much more radio-loud
than the other ULXs in the Laor & Behar (2008) sample. These objects include
NGC 5408 X-1, which is likely to be a stellar-mass object accreting at super-
Eddington rates and driving a wind nebula (Pinto et al., 2016; Luangtip et
al., 2021); Homberg II X-1, which is unlikely to be $>100M_{\odot}$ and
accreting at slightly sub-Eddington (Goad et al., 2006; Ambrosi et al., 2022);
M 82 X1, whose nature is uncertain but may be a few$\times 100M_{\odot}$ black
hole (Pasham et al., 2014; Mondal et al., 2022); and NGC 7424 ULX2, which is
located in a young OB association but does exhibit a steep radio spectrum like
our source (Soria et al., 2006). In short, most of these objects are either
stellar mass objects, or at least are well below the mass range established
for CXO J1338+04 by previous investigations:
$10^{3.5}M_{\odot}<M_{\mathrm{BH}}<10^{6.3}M_{\odot}$ from Mezcua et al.
(2018) and $10^{5}M_{\odot}$ by Kim et al. (2020). In fact, the majority of
ULXs may be stellar mass objects accreting at or above the Eddington rate
(e.g., Berghea et al., 2008), so if CXO J1338+04 is a bona-fide intermediate-
mass black hole producing a real analog of a radio-loud quasar jet, we might
expect its $L_{R}/L_{X}$ ratio to exceed that of other ULXs, as we observe
here.

##  5 Conclusions

We have presented a new high-frequency 22 GHz radio observation of the IMBH
candidate CXO J133815.6+043255, an ultra-luminous X-ray source in the
outskirts of the Seyfert galaxy NGC 5252. We find the following:

  * •

The 22 GHz flux density is $S_{\nu}=0.53\pm 0.02$ mJy. When combined with
archival observations, this results in a broadband SED well-fit by a power law
with a spectral index of $\alpha=-0.66\pm 0.02$ between 1.4 and 22 GHz,
consistent with steep radio spectra from synchrotron jet emission.

  * •

The $L_{R}/L_{X}$ ratio definitively occupies the regime of LLAGN and radio-
loud quasars, and is not consistent with radio-quiet Seyferts or with ULXs
associated with stellar mass objects.

We conclude that the 22 GHz observations support the conclusion that CXO
J133815.6+043255 is an intermediate-mass black hole producing a radio jet, as
suggested by the VLBI observations of Mezcua et al. (2018). That is, CXO
J133815.6+043255 is likely to be a true low-mass analog of radio-loud quasars.

KLS and MM gratefully acknowledge discussions fostered by the _VLA Sky Survey
in the Multiwavelength Spotlight_ conference in Socorro, NM in September 2022.
AT is supported by NASA grant number 80NSSC22K0741. This research has made use
of the CIRADA cutout service at URL cutouts.cirada.ca, operated by the
Canadian Initiative for Radio Astronomy Data Analysis (CIRADA). CIRADA is
funded by a grant from the Canada Foundation for Innovation 2017 Innovation
Fund (Project 35999), as well as by the Provinces of Ontario, British
Columbia, Alberta, Manitoba and Quebec, in collaboration with the National
Research Council of Canada, the US National Radio Astronomy Observatory and
Australia’s Commonwealth Scientific and Industrial Research Organisation.

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