# Modeling Bilingual Sentence Processing: Evaluating RNN and Transformer
Architectures for Cross-Language Structural Priming

First Author
Affiliation / Address line 1
Affiliation / Address line 2
Affiliation / Address line 3
email@domain
&Second Author
Affiliation / Address line 1
Affiliation / Address line 2
Affiliation / Address line 3
email@domain
Bushi Xiao*, Chao Gao*, Demi Zhang*
University of Florida
{xiaobushi, gao.chao<EMAIL_ADDRESS>

###### Abstract

This study evaluates the performance of Recurrent Neural Network (RNN) and
Transformer in replicating cross-language structural priming—a key indicator
of abstract grammatical representations in human language processing. Focusing
on Chinese-English priming, which involves two typologically distinct
languages, we examine how these models handle the robust phenomenon of
structural priming, where exposure to a particular sentence structure
increases the likelihood of selecting a similar structure subsequently.
Additionally, we utilize large language models (LLM) to measure the cross-
lingual structural priming effect. Our findings indicate that Transformer
outperform RNN in generating primed sentence structures, challenging the
conventional belief that human sentence processing primarily involves
recurrent and immediate processing and suggesting a role for cue-based
retrieval mechanisms. Overall, this work contributes to our understanding of
how computational models may reflect human cognitive processes in multilingual
contexts.

Modeling Bilingual Sentence Processing: Evaluating RNN and Transformer
Architectures for Cross-Language Structural Priming

Bushi Xiao*, Chao Gao*, Demi Zhang* University of Florida {xiaobushi,
gao.chao<EMAIL_ADDRESS>

##  1 Introduction

Existing studies show that RNN, particularly Gated Recurrent Unit models, have
been pivotal in modeling human sentence processing (for a review, see Frank et
al., 2019). They can be used to explain phenomena like garden-path effects and
structural priming. These models process sequential information through
recurrence, a characteristic thought to resemble human cognitive processing.
However, the Transformer model, which uses self-attention mechanisms instead
of recurrence, challenges this notion. Transformer’s ability to directly
access past input information, regardless of temporal distance, offers a
fundamentally different approach from RNN.

The effectiveness of Transformer and recent large language models (LLM) in
various natural language processing (NLP) tasks made us wonder if they can
emulate RNN in modeling cross-language structural priming. Prior
psycholinguistics studies show a specific sentence structure in one language
influences the use of similar structures in another language. Existing studies
have demonstrated RNN’s proficiency in capturing properties of human syntactic
processing in multilingual contexts, exhibiting structural priming effects
akin to those observed in human bilinguals Frank (2021). However, the ability
of Transformers and LLM in this domain remains unexplored.

##  2 Literature Review

Structural priming refers to the phenomenon where encountering a specific
syntactic structure boosts the probability of generating or understanding
sentences with a comparable structure Pickering and Ferreira (2008). It is
possible to identify the presence of structural priming effects in language
models, which are analogous to those observed in human language processing
(Prasad et al., 2019; Van Schijndel and Linzen, 2018). The manifestation of
such priming effects in language models suggests that they develop implicit
syntactic representations that may be akin to those employed by the human
language system according to Linzen and Baroni (2021). This phenomenon is too
abstract to be widely known, so in this paper, we aim to visually demonstrate
it through quantitative measure of designed experiments.

Some researchers have already conducted studies in this field. Frank (2021)
indicates the technical feasibility of RNN model to capture cross-language
syntax by calculating the word suprisal value. While study conducted by
Michaelov et al. (2023) demonstrates that the grammatical representations
encoded in large language models exhibit a remarkable degree of similarity.
However, no study has yet compared RNN, Transformer and large language models
in their ability to model cross-language structural priming. This is one of
the reasons why we are undertaking this study.

In the meanwhile, researches on crosslingual structural priming has also made
progress. Loebell and Bock (2003) offer evidence for a structural source of
priming between German and English. Shin and Christianson (2009) suggest that
structures that are identical in the Korean and English language have a
single, shared mental representation. However, there is little evidence about
this phenomenon on the two entirely different language families (English:
Indo-European family, Chinese: Sino-Tibetan family), we believe that there are
still some clues we can found in this two languages.

Figure 1: The workflow of analysis of the structural priming environment is
shown. Part (a) involves preprocessing the raw bilingual data through several
steps, resulting in the generation of a lexicon and subsequent division into a
training set and a test set. In part (b), a Transformer/RNN-based encoder-
decoder architecture is employed. In part (c), the trained model’s performance
is assessed using the BLEU metric. Then, the model’s performance can be
evaluated across the four subsets of the test set, providing insights into its
structural priming quality across different sentence structures.

##  3 Data Preparation

We have selected and processed a Chinese-English corpus, which contains
millions of pairs of Chinese and English parallel texts. The source can be
found here. We employ a DataLoader to facilitate batch processing,
transforming text into token IDs suitable for model interpretation. We utilize
the Helsinki-NLP tokenizer, specifically designed for Chinese to English
mapping, capable of accommodating over a thousand models for diverse language
pairs.

The tokenizer by default processes text according to source language settings.
To encode target language text, the context manager as_target_tokenizer() must
be used. Without this, the source language tokenizer would be applied
incorrectly to the target text, leading to poor tokenization results, such as
improperly splitting words unrecognized in the source language.

For sequence-to-sequence models, it’s essential to set padding tokens to -100
to ensure they are ignored during loss calculations. This setup is crucial for
training the models effectively, allowing for precise adjustment of model
parameters based on the tokenized input and target sequences. This
preprocessing step ensures that the data fed into the model is properly
formatted, facilitating optimal training outcomes.

As for the test set, we designed and collected the test set ourselves. The
data is structured into a five-column format, where the first column consists
of Chinese sentences, followed by four English sentences. These sentences are
categorized into two distinct groups based on their semantic and syntactic
properties relative to the Chinese sentences. We collected 4 types of diverse
sentence structures (Double Object, Prepositional Object, Active Voice,
Passive Voice) to form the test set.

The first category contains English sentences that semantically match the
Chinese sentences. In this category, the first English sentence maintains the
same syntactic structure as its Chinese counterpart, while the second sentence
presents a different syntactic arrangement. For instance, the pair "This cake
was burnt by mom." and "Mom burnt this cake." illustrates this category where
the first sentence is in passive voice (mirroring the structure of the Chinese
sentence), and the second is in active voice.

The second category comprises English sentences that do not semantically align
with the Chinese sentences. Despite the semantic disparity, one sentence in
each pair preserves the syntactic form of the original Chinese sentence (e.g.
both are in passive voice), and the other varies syntactically. This
arrangement allows us to investigate the influence of semantic mismatches on
syntactic priming, providing insights into whether structural similarities
override semantic discrepancies in cognitive processing.

This dataset will serve as the foundation for our experiments on structural
priming, aiming to explore how linguistic structure and semantics influence
sentence formulation in bilingual speakers.

##  4 Language Models

As depicted in part b of Fig. 1, we implemented both a Transformer model and
an RNN model to handle sequence-to-sequence tasks using the encoder-decoder
architecture. This architecture enables us to process input sequences of
varying lengths and generate output sequences of different lengths, which is
crucial for us to pay attention to sentences with different structures yet
similar meanings. In this section, we will primarily delve into why these
language models can assist us in identifying structural priming in detail.

###  4.1 Transformer

In the transformer model, we employ the self-attention mechanism to store the
sentence structure, which can capture dependencies between different positions
and adjusting the representation of each word based on its relationship with
others, thus facilitating the learning of sentence structure. We can describe
this mechanism with:

$\text{Attention}(Q,K,V)=\text{softmax}\left(\frac{QK^{T}}{\sqrt{d_{k}}}\right)V$
(1)

where $Q,K,V$ are obtained through linear transformations of an input sequence
of text, each with its own learnable weight matrix. In the encoder part of
model, $Q,K,V$ comes from the same source sequence, while in the decoder part,
$Q$ comes from the target sequence, and $K$ and $V$ come from the output
sequence of the encoder. Since the computation of $Q$, $K$, and $V$ requires
processing the entire input sentence, the model can simultaneously focus on
all positions and capture the structure of the sentence.

Additionally, in decoder part, employing multiple attention heads allows us to
capture diverse levels of sentence features, so that we can get a more
comprehensive representation of sentence structure. Each attention head
specializes in capturing specific semantic relationships, such as word
dependencies and distance relationships. This approach enhances the model’s
ability to comprehend the intricacies of sentence structure. The equation is
as follows:

$\text{MH}(Q,K,V)=\text{Concat}(head_{1},\ldots,head_{h})\cdot W^{O}$ (2)

where $W^{O}$ is the weight matrix we need to train.
$head_{1},\ldots,head_{h}$ is computed through the attention equation 1 and
represents the attention weights of each head, and in our project we choose to
use 8 heads. Concat is the operation of concatenating tensors along their last
dimension.

Furthermore, we also pay attention to selecting the positional encoding. While
the common method involves using Sine and Cosine Functions, in our project, we
opt for the Learnable Positional Embedding from the Hugging Face Transformers
library. We believe this approach offers more advantages for learning
structural priming because it makes our model can better understand and encode
the relative positions of words within a sentence. In contrast to the fixed
positional encoding, learnable positional embeddings assign different weights
to different positions, emphasizing the relevant positional information that
contributes to the priming effect. This enables the model to capture more
intricate positional relationships and dependencies specific to the task of
structural priming.

###  4.2 Recurrent Neural Network

We believe that recurrent neural networks (RNN) can also preserve the sentence
structure and help us find the structural priming environment, because its
sequential nature can process input token from a previous contextual
understanding of the entire sentence. As each token is processed, the hidden
state of the RNN is updated, retaining information about the preceding tokens
and their contextual relevance. This sequential processing enables the model
to capture dependencies relationships between words, thereby preserving the
structural integrity of the sentence, and we can summarize the equation as
follows:

$\text{State}(dh_{i},c_{i}),p=f(\text{State}(dh_{i-1},c_{i-1}),m)$ (3)

where function $f$ means the hidden layer of the RNN model, which is a neural
network. It takes the previous layer’s state State i-1 and the output vector
from the previous time step $m$ as input, and outputs the next layer’s state
State i and prediction value $p$ until it encounters the termination symbol.
In the state, $dh$ signifies the hidden state of the RNN unit in decoder,
tasked with capturing pertinent information gleaned from the input sequence.
In the initial decoder step, it embodies the ultimate output state of the
encoder, while in subsequent decoder steps, it denotes the output of the
previous RNN unit.

In addition, to solve the limitation of not being able to remember the entire
sentence structure, we introduce the attention mechanism. The attention
mechanism of the RNN model can make the model pay more attention to the part
of the input sequence that is most relevant to the current output, thereby
improving the accuracy of prediction. It has the potential to be applied to
structural initiation prediction because of its ability to capture
dependencies in sequence data and the ability to exploit these dependencies
for prediction. As shown in the equation 3, we use $c$ to represent the
attention. The calculation of $c$ is showed as follows:

$\alpha_{i}=g(eh_{i},dh_{0})$ (4)

As I mentioned before, $dh_{0}$ means the ultimate output state of the encoder
and $eh$ signifies the hidden state of the each RNN unit in encoder, function
$g$ is used to calculate the weight $alpha_{i}$ of $eh_{i}$ in the final state
$dh_{0}$.

And as a result, we can get the attention $c$ by combine all of the previous
state together:

$c_{i}=\sum(\alpha_{i}*dh_{i})$ (5)

calculated by summing the products of the weight $\alpha$ and the state in
decoder $dh$.

##  5 Experimental Setup

To assess the quality of the performance of our model in Chinese-English, we
adopted the standard bilingual evaluation understudy (BLEU) algorithm. The
BLEU score ranges from 0 to 1, which indicates the similarity of predicted
text against target text.

$\text{BLEU}=\text{BP}\cdot\exp\left(\sum_{n=1}^{N}w_{n}\log p_{n}\right)$

Where: $N$ is the maximum n-gram order (typically 4). $w_{n}$ is the weight
assigned to each n-gram precision score.($\sum_{n=1}^{N}w_{n}=1$) $p_{n}$ is
the precision score for n-grams of order. $n$ BP is the brevity penalty, which
penalizes shorter results.

Post the generation of predicted outcomes and the assembly of a test set, we
analyzed the relationship between the predicted sentences and four different
types of reference sentences: (1) correct mappings with the same structure,
(2) semantically similar but structurally different sentences, (3)
semantically different but structurally identical sentences, and (4) sentences
that are different both semantically and structurally.

We categorized the comparisons into two distinct groups based on semantic
similarity. In the first category, encompassing sentences with identical
meanings, we hypothesized that if structural priming is effective, the BLEU
scores between the predicted sentences and the reference sentences with the
same structure should be higher than those with different structures. This
comparison aimed to establish whether the model exhibits a preference for
reproducing structures that are syntactically aligned with the ground truths
when the semantic content remains constant.

The second category involved sentences that differed in meaning. This category
is particularly crucial for demonstrating structural priming, as the influence
of semantic similarity is negated. Here, a higher BLEU score for sentences
with identical structures compared to those with different structures would
strongly indicate that the model’s predictions are influenced by the
structural aspects of the input, irrespective of semantic changes.

Through this methodology, we sought to rigorously test the presence of
structural priming in outputs, offering insights into how structural
properties of language are processed and replicated by the models.

Figure 2: To test and calibrate, we first compare with the ground truths.

##  6 Results Analysis

We present the performance of the GRU based RNN and vanila transformer model
we trained for the tasks, and then demonstrate their crosslingual structural
priming effect in Chinese-English bilingual scenarios. We also present our
insights for the response of open-source large language models on the same
dataset accordingly.

###  6.1 Structural Priming Performance

Our comparative analysis highlights that while both models achieve competitive
BLEU scores, the transformer model demonstrates a slight advantage in handling
complex sentence structures.

As depicted in Fig. 2, we can observe that, when the training data is
sufficiently large, the predicted BLEU scores of both models relative to the
standard structured sentence segments reached relatively high levels.

Figure 3: Compare the predictions to the correct cross-language priming
example and calculate the average BLEU Score. Figure 4: Compare the
predictions to the opposite cross-language priming example and calculate the
average BLEU Score. Figure 5: Count and calculate the proportion of correct
cross-language priming chunks in the machine prediction results.

###  6.2 Crosslingual Structural Priming Effect

Through our examination of crosslingual structural priming, we observed a
noteworthy pattern: both models facilitated the use of target-language
syntactic structures influenced by the source language. However, the
transformer model displayed a more pronounced priming effect, indicating a
potential edge in mimicking human-like syntactic adaptation in bilingual
contexts.

Figure 6: Cross-lingual structural priming effect for large language models in
the context of Chinese-English with various sentence types. Top: Normalized
probability of true target verus false target prediction based on the next
word logit value. Bottom: summary of each comparison shown in the same raw
above. From left to right: XGLM model with parameters of 564M, 1.7B, 2.9B,
4.5B on active, passive, prepositional, and Double tasks.

As depicted in Fig. 3 and Fig. 4, the BLEU scores of machine-generated
predictions with both correct and opposite priming test sets, we gain insights
into model performance. Specifically, we evaluate the similarity levels
between the model predictions and the correct priming test sets (e.g., Active-
Active, DO-DO) as well as the opposite priming test sets (e.g., Active-
Passive, PO-DO). Higher BLEU scores against the correct priming test sets
indicate that the model predictions align more closely with the appropriate
structural priming, while higher scores against the opposite priming test sets
suggest the model deviates from the expected priming behavior.

The experimental results reveal that when evaluated against the correct
priming test sets, the Transformer model exhibits similar levels to GRU, with
slight improvements being observed as the n-gram size increases. Conversely,
in comparison to opposite priming, GRU generally outperforms the Transformer.
Given that this comparison involves what is termed as "incorrect" priming,
where GRU aligns more closely with the opposite priming test set, we infer
that the Transformer adheres more closely to the appropriate structural
priming.

In a previous study, Michaelov et al. (2023) examined the presence of
structural priming by comparing the proportion of target sentences produced
after different types of priming statements. Similarly, for each experimental
item, we prime the language model with the priming sentence and calculate the
normalized probabilities of the two target sentences. We calculate these
normalized probabilities in the following way:

First, calculate the raw probability of each target sentence given the priming
sentence:

$\displaystyle P(\text{DO Target}|\text{DO Prime})$ $\displaystyle P(\text{PO
Target}|\text{PO Prime})$ $\displaystyle P(\text{DO Target}|\text{PO Prime})$
$\displaystyle P(\text{PO Target}|\text{DO Prime})$

And the same method for:

$\displaystyle P(\text{Active Target}|\text{Active Prime})$ $\displaystyle
P(\text{Passive Target}|\text{Passive Prime})$ $\displaystyle P(\text{Active
Target}|\text{Passive Prime})$ $\displaystyle P(\text{Passive
Target}|\text{Active Prime})$

Then, these probabilities are normalized to calculate the conditional probability of the target sentence if the model output is one of the two target sentences, taken DO | PO as example:

$P_{N}(\text{Target}|\text{Prime})=\
\frac{P(\text{Target}|\text{Prime})}{P(\text{DO
Target}|\text{Prime})+P(\text{PO Target}|\text{Prime})}$

Since the sum of the normalized probabilities of the two target sentences is
1, we only need to consider the probability of one target type and compare
between different priming types. The reason is that the probability of another
target type can be derived from this, i.e.
$P_{N}(\overline{\text{Target}}|\text{Prime})=1-P_{N}(\text{Target}|\text{Prime})$.
By considering only one goal type, we can directly compare the priming effects
of the two priming types on that goal type, which is the main focus of
analysis in structural priming research. The quantitative comparative findings
depicted in Fig. 5 derived from the sentence chunk dimension reveal that the
Transformer model generally outperforms GRU. Through a horizontal examination
of priming structural types, it is evident that machine predictions exhibit
superior performance with respect to active/passive structures compared to
those of PO/DO.

The Transformer model we trained was only exposed to Chinese-English dataset.
It has been demonstrated that the LLM can mimic human language structural
priming effect in various scenarios – both within-language and crosslingual
experiments. However, the evidence of such multilingual language models for
Chinese-English structural priming effect is missing. We adopted XGLM models
proposed by Lin et al. (2022) to calibrate their outcome based on the
normalized score defined above on the same set of tasks we designed for RNN
and transformers. Among the four categories in our experiments, we found this
language model family is more sensitive in showing structural priming effect
in passive and prepositional tasks (see Fig. 6), while the effect is more
noticeable in the former case.

##  7 Discussion and Conclusion

The current study evaluates cross-language structural priming effect in RNN
and Transformer models in the setting of Chinese-English. We find evidence for
abstract crosslingual grammatical representations in the models that function
similarly to those found in previous studies.

###  7.1 Interpretation of Results

Our results demonstrate a decrease in BLEU scores with the increasing length
of n-grams, a trend that is consistent with existing findings in sentence-
similarity evaluation He et al. (2022). As n-grams become longer—from unigrams
to bigrams, trigrams, they encapsulate more specific linguistic contexts,
making exact matches less likely unless the target sentence is highly precise.
Moreover, any minor errors in word choice or sequence can disrupt the
alignment of these longer n-grams.

Importantly, our results indicate that transformer models outperform RNN in
modeling Chinese-English structural priming, a finding that is intriguing
given prior research. Traditionally, RNNs have been effective in modeling
human sentence processing, capable of explaining phenomena such as garden-path
effects and structural priming through their sequential processing
capabilities, which are thought to mirror aspects of human cognitive
processing Frank (2021).

This superiority of transformers raises questions about the efficacy of RNNs
as models of human sentence processing, especially if they are surpassed by a
model considered less cognitively plausible. However, it is possible to
interpret the results as supportive of the cognitive plausibility of
transformers, particularly due to their attention mechanism. While the concept
of unlimited working memory in transformers is viewed as implausible, some
researchers argue that actual human working memory capacity is much smaller
than traditionally estimated—limited to only two or three items—and that
language processing involves rapid, direct-access retrieval of items from
memory Lewis et al. (2006), a process compatible with the attention mechanism
in transformers. This mechanism assigns weights to previous inputs based on
their relevance to the current input and aligns with cue-based retrieval
theories, which suggest that memory retrieval is influenced by the similarity
of current cues to stored information Parker and Shvartsman (2018).

###  7.2 Limitations

Moreover, it is important to note that our study does not include a comparison
with human data on Chinese-English priming. We equate the models’ ability to
replicate cross-language priming with the structural "correctness" of their
outputs, yet empirical studies indicate that even humans do not achieve full
priming rate Hsieh (2017). Therefore, it is conceivable that if the models’
outputs were compared directly to human data, RNNs might more closely resemble
human performance. This limitation highlights an area for future research,
which could involve direct comparisons to human priming data to better assess
the models’ fidelity to human language processing.

A further limitation of our study is that our models are not capable of
generating sentences based on novel word concepts and thematic roles, which is
a task typically performed by human participants in priming studies. In such
studies, participants are exposed to a specific sentence structure and then
asked to describe a new event, depicted in an image, using that structure
Hsieh (2017). Consequently, some critics may argue that what our models
essentially do is translate from Chinese to English without generating new
semantic content, as the semantic information remains consistent from the
priming sentence to the output sentence. Despite these critiques, we maintain
that the current study design still validly assesses the priming effect. This
is because the models must choose which sentence structure to use from among
various structures that share the same semantic content, a choice influenced
by the priming effect.

However, we acknowledge that our design is susceptible to the "lexical boost"
effect, where the structural priming effect is intensified when the same
lexical head is repeated in both the prime and target sentences Pickering and
Branigan (1998). For instance, if the target sentence is "Alice gave Bob a
book," the priming effect is more pronounced if the prime sentence was "Carl
gave Danis a letter" rather than "Alice showed Bob a book." Given that the
semantic content remains constant across the prime and output sentences in our
study, the observed priming effect is artificially strengthened compared to
what might be observed in a pure priming task.

###  7.3 Future Directions

To address this issue, we can develop a model that can produce sentences based
on new individual semantic concepts and thematic roles before and after
priming. Although developing such models could be challenging, it could free
us from the lexical boost effect. Alternatively, we could shift our focus from
production to comprehension. By measuring the surprisal levels in models, we
can gain insights into how structural priming influences model comprehension,
as suggested in recent studies Merkx and Frank (2021). In information theory
and psycholinguistics, surprisal is a measure of how unexpected a word is in a
given linguistic context. The more unexpected the word, the higher its
surprisal value. For instance, if a model consistently shows lower surprisal
at structurally complex points in sentences that follow a priming example, it
would suggest that the priming has effectively prepared the model for these
structures. This method offers a way of understanding how structural priming
impacts language processing in models, free from the confounding effects of
repeated vocabulary.

Additionally, there is evidence suggesting an inverse relationship between the
frequency of linguistic constructions and the magnitude of priming effects
observed with those constructions (Jaeger and Snider, 2013; Kaschak et al.,
2011). For example, the direct object (DO) construction is more common in
American English than the prepositional object (PO) construction Bock and
Griffin (2000). Studies have shown that the less frequent PO construction
exhibits stronger priming effects compared to the more frequent DO
construction Kaschak et al. (2011). This aligns with theories of implicit
learning in structural priming, where more frequently encountered structures
are less ’surprising’ to the language system and thus generate weaker priming
effects. To further explore this, we could train models on corpora consisting
of American versus British English, which differ in their usage frequencies of
certain constructions, to see if a similar inverse frequency effect is
observed in computational models. This approach would help illuminate the
dependency of structural priming on construction frequency, potentially
providing deeper insights into how implicit learning processes are modeled
computationally.

Another aspect worth discussing is the significance of using LLM to simulate
human language processing efforts. As highlighted in the introduction, the
ultimate goal is to deepen our understanding of how the human brain
functions—assuming that models which appear more human-like externally might
also mirror human cognitive processes internally. However, one might question
the validity of using LLM for this purpose. Given that these models often
function as "black boxes," their internal operations remain largely opaque.
Despite their impressive computational abilities, the lack of transparency
means that even if they outperform more interpretable models, they do not
necessarily enhance our understanding of brain function. Previous studies
argue that crosslingual structural priming might be affected by the asymmetry
of training sources in certain language pairs Michaelov et al. (2023). By
measuring the probability changes for source and target sentences, we found
such multilingual auto-regressive Transformer language models display evidence
of abstract structural priming effect despite the fact that it didn’t perform
equally well in all scenarios.

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