#  Know How to Make Up Your Mind! Adversarially Detecting and Alleviating
Inconsistencies in Natural Language Explanations

Myeongjun Jang1 Bodhisattwa Prasad Majumder3 Julian McAuley3
Thomas Lukasiewicz2,1 Oana-Maria Camburu4
1University of Oxford, UK 2Vienna University of Technology, Austria
3University of California San Diego 4University College London, UK
<EMAIL_ADDRESS>
###### Abstract

While recent works have been considerably improving the quality of the natural
language explanations (NLEs) generated by a model to justify its predictions,
there is very limited research in detecting and alleviating inconsistencies
among generated NLEs. In this work, we leverage external knowledge bases to
significantly improve on an existing adversarial attack for detecting
inconsistent NLEs. We apply our attack to high-performing NLE models and show
that models with higher NLE quality do not necessarily generate fewer
inconsistencies. Moreover, we propose an off-the-shelf mitigation method to
alleviate inconsistencies by grounding the model into external background
knowledge. Our method decreases the inconsistencies of previous high-
performing NLE models as detected by our attack.

##  1 Introduction

The accurate yet black-box nature of deep neural networks has accelerated
studies on explainable AI. The advent of human-written natural language
explanations (NLEs) datasets Wiegreffe and Marasovic (2021) has paved the way
for the development of models that provide NLEs for their predictions.
However, by introducing an adversarial attack, which we hereafter refer to as
eIA (explanation Inconsistency Attack), Camburu et al. (2020) found that an
early NLE model Camburu et al. (2018) was prone to generate inconsistent NLEs
(In-NLEs). More precisely, two logically contradictory NLEs generated by a
model for two instances that have the same context are considered to form an
inconsistency. For example, assume a self-driving car stops in a given traffic
environment (the context). If the passenger asks the car Q1:“Why did you
stop?”, and it provides NLE1: “Because the traffic light is red.”, and, for
the same context, if the passenger instead asks Q2: “Why did you decide to
stop here?” and the car provides NLE2: “Because the traffic light is green”,
then NLE1 and NLE2 form an inconsistency.

A model that generates In-NLEs is undesirable, as it either has a faulty
decision-making process (e.g., the traffic light was green, so the car should
not have stopped), or it generates NLEs that are not faithfully describing its
decision-making process (e.g., the car stopped for a red traffic light, but
states that it was green) Camburu et al. (2020). While recent high-performing
NLE models have largely improved in terms of the quality (plausibility) of the
generated NLEs, to our knowledge, these models have not been tested against
generating inconsistent NLEs.

In this work, we first propose a fast, efficient, and task-generalizable
adversarial attack that utilizes external knowledge bases. Through experiments
on two datasets and four models, we verify the increased efficiency of our
approach over the eIA attack, the only inconsistency attack for NLE models, to
our knowledge. We also show that the high-performing NLE models are still
prone to generating significantly many In-NLEs and, surprisingly, that a
higher NLE quality does not necessarily imply fewer inconsistencies. Second,
we propose a simple yet efficient off-the-shelf method for alleviating
inconsistencies that grounds any NLE model into background knowledge, leading
to fewer inconsistencies. The code for this paper is available at
https://github.com/MJ-Jang/eKnowIA.

##  2 Inconsistency Attack

We propose eKnowIA (explanations Knowledge-grounded Inconsistency Attack),
which detects more In-NLEs in a faster and more general manner than eIA.

###  2.1 Original eIA Attack

#### Setting.

Given an instance $\mathrm{x}$, Camburu et al. (2020) divide it into: the
context part $\mathrm{x_{c}}$ that remains fixed, and the variable part
$\mathrm{x_{v}}$ that is changed during the attack. For example,
$\mathrm{x_{c}}$ and $\mathrm{x_{v}}$ would be a premise and a hypothesis,
respectively, for natural language inference (NLI — detailed below). Let
$\mathrm{e}_{m}(\mathrm{x})$ denote the NLE generated by a model $m$ for the
input $\mathrm{x}=(\mathrm{x_{c}},\mathrm{\hat{x}_{v}})$. The objective is to
find $\mathrm{\hat{x}_{v}}$ such that $\mathrm{e}_{m}(\mathrm{x})$ and
$\mathrm{e}_{m}((\mathrm{x_{c}},\mathrm{\hat{x}_{v}}))$ are logically
contradictory (see examples in Table 10).

#### Steps.

The eIA attack has the following steps:

  1. 1.

Train a neural model to act as a reverse explainer, called RevExpl, that takes
$\mathrm{x_{c}}$ and $\mathrm{e}_{m}(\mathrm{x})$ as input and generates
$\mathrm{x_{v}}$, i.e.,
RevExpl$(\mathrm{x_{c}};\mathrm{e}_{m}(\mathrm{x}))=\mathrm{x_{v}}$.

  2. 2.

For each generated NLE $\mathrm{e}_{m}(\mathrm{x})$:

    1. (a)

Automatically create a set of statements $\mathcal{I}_{e}$ that are
inconsistent with $\mathrm{e}_{m}(\mathrm{x})$.

    2. (b)

For each $\hat{e}\in\mathcal{I}_{e}$, generate a variable part
$\mathrm{\hat{x}_{v}}=$ RevExpl$(\mathrm{x_{c}};\hat{e})$.

    3. (c)

Query $m$ on $\hat{x}=(\mathrm{x_{c}},\mathrm{\hat{x}_{v}})$ to get
$\mathrm{e_{m}}(\hat{\mathrm{x}})$.

    4. (d)

Check whether $\mathrm{e_{m}}(\hat{\mathrm{x}})$ is indeed inconsistent with
$\mathrm{e_{m}}(\mathrm{x})$ by checking whether
$\mathrm{e_{m}}(\hat{\mathrm{x}})$ is included in $\mathcal{I}_{e}$.

#### Creating $\mathcal{I}_{e}$.

Camburu et al. (2020) used simple elimination of negation (removing “not” or
“n’t”) and a task-specific template-based approach for this step. For the
template-based approach, they manually create a set of label-specific
templates for NLEs such that introducing the instance-specific terms of an NLE
from one template into any template from another label creates an
inconsistency. They illustrate this process only on the e-SNLI dataset
(Camburu et al., 2018), leaving room to question how easily it generalizes to
other datasets. e-SNLI contains NLEs for the SNLI dataset Bowman et al.
(2015), where the NLI task consists in identifying whether a premise and a
hypothesis are in a relation of entailment (if the premise entails the
hypothesis), contradiction (if the hypothesis contradicts the premise), or
neutral (if neither entailment nor contradiction hold). Examples of their
templates are: “<X> is <Y>” (for entailment) and “<X> cannot be <Y>” (for
contradiction). Based on the templates, for a $\mathrm{e}_{m}(\mathrm{x})$ of
“A dog is an animal.”, an inconsistent statement of “A dog cannot be an
animal” is obtained (<X> = “A dog”, <Y> = “an animal”). They manually
identified an average of 10 templates per label.

###  2.2 Our eKnowIA Attack

The template-based approach in eIA has two major drawbacks: (1) requires
substantial human effort to find an exhaustive set of templates for each
dataset, (2) many different ways of obtaining inconsistencies (e.g., using
antonyms) are not taken into account. Moreover, even their negation rule can
also be improved. To alleviate these drawbacks, we adopt three rules.

#### Negation.

We remove and add negation tokens to negated and non-negated sentences,
respectively. To avoid grammatical errors, we add one negation per sentence
only if the sentence belongs to one of the following two templates:

  * •

<A> is <B>, <A> are <B> (add “not”),

  * •

<A> has <B>, <A> have <B> (add “does/do not” only if <B> is a noun).

#### Antonym replacement for adjectives/adverbs.

We replace adjectives/adverbs with their antonyms from ConceptNet Speer et al.
(2017) (using the NLTK POS tagger). Only one adjective or adverb at a time is
replaced for each NLE, to avoid deteriorating the contradictory meaning.
Employing other abundant thesauruses could improve our approach, which we
leave as future work.

#### Unrelated noun replacement.

We replace a noun with an unrelated one, e.g., “human” with “plant”. This is
only applied to the noun that is the last word of the sentence, to reduce the
possibility of false inconsistencies as the part-of-speech (POS) tagger
occasionally made incorrect predictions for words in the middle of a sentence.
To get unrelated nouns, we use the DistinctFrom and Antonym relations in
ConceptNet. However, we noticed that ConceptNet contains noisy triplets where
the subject and object are not antonyms, such as “man” and “people” for
“person”111A pair of words with opposite meanings (from Wikepedia).. To
avoid these, we created a list (see Table 8 in the appendix) of triplets from
ConceptNet to be ignored, by manually investigating a random subset of 3000
detected inconsistencies. While this involved human effort, we highlight that
this is due to the nature of ConceptNet and other knowledge bases with more
accurate instances may be used instead. However, since we only found eight
noisy triplets, we decided to keep ConceptNet, which otherwise worked well for
our datasets. Finally, we also noticed that our rules may not lead to In-NLEs
if both the context and variable part contain negations. Examples are in Table
9 in the appendix. We filter out such pairs.

Model | e-SNLI | Cos-E
---|---|---
Acc. | $\mathcal{S}_{r}$ | $\mathcal{H}_{r}$ | e-ViL | Acc. | $\mathcal{S}_{r}$ | $\mathcal{H}_{r}$ | e-ViL
NILE | 90.7 | 3.13 | 2.27 | 0.80 | - | - | - | -
KnowNILE | 90.9 | 2.42$\dagger$ | 1.99$\dagger$ | 0.82 | - | - | - | -
CAGE | - | - | - | - | 61.4 | 0.42 | 0.06 | 0.43
KnowCAGE | - | - | - | - | 62.6 | 0.11$\dagger$ | 0.01$\dagger$ | 0.44
WT5-base | 90.6 | 12.88 | 1.70 | 0.76 | 65.1 | 0.95 | 0.12 | 0.55
KnowWT5-base | 90.9 | 11.45 | 1.19$\dagger$ | 0.80$\dagger$ | 65.5 | 0.84$\dagger$ | 0.09$\dagger$ | 0.56
Table 1: Results of our eKnowIA attack and our method for mitigating In-NLEs. The best results for each pair of (model, Know-model) are in bold; $\mathcal{S}_{r}$ and $\mathcal{H}_{r}$ are given in %; $\dagger$ indicates that Know-models showed statistically significant difference with p-value < 0.05 ($\dagger$) using the t-test. Dataset | Method | Time | $\mathcal{S}_{r}$ | $\mathcal{H}_{r}$
---|---|---|---|---
e-SNLI | eIA | 10 days | 2.19 | 384/24M
eKnowIA | 40 min | 12.88 | 1,494/88K
Cos-E | eIA | 2.5 days | 0.32 | 5/5M
eKnowIA | 5 min | 0.95 | 13/11K
Table 2: Comparison between eIA and eKnowIA on WT5-base. The best results are
in bold; $\mathcal{S}_{r}$ is given in %; $\mathcal{H}_{r}$ values are in
fractions to emphasise the high denominators of the eIA.

###  2.3 Experiments

#### Datasets.

We consider two tasks: NLI (with the e-SNLI dataset described in Sec. 2.1) and
commonsense question answering (CQA). The Cos-E 1.0 dataset (Rajani et al.,
2019) contains CQA instances formed of a question, three answer candidates,
and an NLE for the correct answer. The objective of the Cos-E (Rajani et al.,
2019) dataset is to select an answer among the three candidates given a
question and to generate an NLE to support the answer. Following Camburu et
al. (2020), we set the premise as context and the hypothesis as the variable
part for e-SNLI. For Cos-E, to avoid omitting the correct answer, we set the
question and the correct answer as the context, and the remaining two answer
candidates as the variable part. Just like eIA, our attack is solely intended
for detecting In-NLEs and not as a label attack (which may or may not happen).

#### Evaluation metrics.

Let $\mathcal{I}_{e}$ be generated at Step 2a for each instance in a test set
$\mathcal{D}_{test}$, and let $\mathcal{I}_{s}\subseteq\mathcal{I}_{e}$ be the
set of detected In-NLEs (after Step 2d). For each instance, our attack can
identify multiple inconsistencies (via multiple variable parts). We,
therefore, use two evaluation metrics: hit-rate ($\mathcal{H}_{r}$) and
success-rate ($\mathcal{S}_{r}$):

$\displaystyle\mathcal{S}_{r}={N_{c}}/{|\mathcal{D}_{test}|}\text{ and
}\mathcal{H}_{r}={|\mathcal{I}_{s}|}/{|\mathcal{I}_{e}|},$

where $N_{c}$ is the number of unique instances for which the attack
identified at least one inconsistency. Intuitively, $\mathcal{S}_{r}$ denotes
the ratio of the test instances where the attack is successful, while
$\mathcal{H}_{r}$ denotes the ratio of detected In-NLEs to that of the
proposed In-NLEs.

#### Models.

We consider the following high-performing NLE models, with their
implementation detailed in Appendix A.1: NILE Kumar and Talukdar (2020) for
NLI, CAGE Rajani et al. (2019) for CQA, and WT5-base (220M parameters) Narang
et al. (2020) for both tasks. WT5 models with more parameters (e.g., WT5-11B)
would require considerably more computing while providing relatively small
gains in NLE quality (32.4 for WT5-base vs. 33.7 for WT5-11B (Narang et al.,
2020)). Therefore, they are not considered here due to limited computing
resources. Implementation details are given in Appendix A.1.

###  2.4 Results

#### eKnowIA vs. eIA.

We compare eKnowIA with eIA only on the WT5-base model, since eIA requires a
prohibiting amount of time. As in Camburu et al. (2020), we manually verified
the naturalness of adversarial hypotheses on 50 random samples for each model.
Sentences that go against common sense are considered unnatural. Minor
grammatical errors and typos are ignored. We observe that 81.5% of the
adversarial hypotheses were natural, on average, for each model. Details are
in Appendix A.4. The results are summarized in Table 2. The e-SNLI results are
adjusted to reflect the proportion of natural adversarial hypotheses by
multiplying the number of detected pairs of In-NLEs for each model with the
estimated naturalness ratio. For Cos-E, an unnatural variable part would
consist of stop words or a repetition of another answer candidate. We
automatically found 2 out of 22 examples to be unnatural, which were removed.
We observe that eIA generates a tremendous amount of inconsistent candidates
($\mathcal{I}_{e}$), e.g., 24M for e-SNLI, thus being extremely slow (e.g., 10
days vs. 40 min for eKnowIA), while also obtaining lower $\mathcal{S}_{r}$ and
$\mathcal{H}_{r}$ than eKnowIA (e.g., 2.19% vs. 12.88% $\mathcal{S}_{r}$).

Premise: A man is riding his dirt bike through the air in the desert.
---
Hypothesis: A man is on a motorbike |  Hypothesis: The man is riding a motorbike.
Predicted Label: entailment |  Predicted Label: contradiction
Explanation: A dirt bike is a motorbike. |  Explanation: A dirt bike is not a motorbike.
Question: John knew that the sun produced a massive amount of energy in two
forms. If you were on the surface of the sun, what would kill you first?
Choices: heat, light, life on earth |  Choices: heat, light, darkness
Predicted Label: heat |  Predicted Label: heat
Explanation: the sun produces heat and light. |  Explanation: the sun produces heat and darkness.
Table 3: Examples of inconsistent NLEs detected by eKnowIA for WT5 on e-SNLI
and CAGE on Cos-E. The first column shows the original variable part, and the
second column shows the adversarial one. Premise: A man is riding his dirt
bike through the air in the desert.
---
Hypothesis: A man is on a motorbike |  Hypothesis: The man is riding a motorbike.
Predicted Label: entailment |  Predicted Label: entailment
Extracted Knowledge: {dirt bike, IsA, motorcycle}, {desert, MannerOf, leave}, {air, HasA, oxygen} |  Extracted Knowledge: {dirt bike, IsA, motorcycle}, {desert, MannerOf, leave}, {air, HasA, oxygen}
Explanation: A dirt bike is a motorbike. |  Explanation: A dirt bike is a motorbike.
Question: John knew that the sun produced a massive amount of energy in two
forms. If you were on the surface of the sun, what would kill you first?
Choices: heat, light, life on earth |  Choices: heat, light, darkness
Predicted Label: heat |  Predicted Label: heat
Extracted Knowledge: {light, IsA, energy} ,{heat, IsA, energy} |  Extracted Knowledge: {light, Antonym, dark}, {heat, IsA, energy}
Explanation: light and heat are two forms of energy. |  Explanation: the sun produces heat and light.
Table 4: Examples of successfully defended instances by KnowWT5 on e-SNLI and
KnowCAGE on Cos-E. This table should be read together with Table 3 to
appreciate the defence.

#### eKnowIA on NLE models.

The results of eKnowIA applied to NILE, CAGE, and WT5 are in the upper lines
of each block in Table 1. All models are vulnerable to the inconsistency
attack. Also, a better NLE quality may not necessarily guarantee fewer
inconsistencies. For example, WT5-base has a better NLE quality than CAGE on
Cos-E (0.55 vs. 0.43 e-ViL score; see below), but eKnowIA detected more
inconsistencies for WT5-base than for CAGE (0.95 vs. 0.42 success rate).
Examples of generated In-NLEs are in Table 3. More examples are in Tables
10–12 in Appendix A.7. We observe that the In-NLEs usually contradict common
sense, which is aligned with previous studies showing that language models,
used as pre-trained components in the NLE models, often suffer from factual
incorrectness Mielke et al. (2020); Zhang et al. (2021).

##  3 Our Know Method for Alleviating Inconsistencies

Our approach for alleviating inconsistencies in NLE models consists of two
steps: (1) extraction of knowledge related to the input and (2) knowledge
injection.

#### Extracting related knowledge.

We leverage a knowledge extraction heuristic proposed by Xu et al. (2021) as
follows:

  1. 1.

Extract entities from an input’s context part.

  2. 2.

Find all knowledge triplets that contain the entities.

  3. 3.

For each entity, calculate a weight $s_{j}$ for each extracted triplet as:

$s_{j}=w_{j}\times{N}/{N_{r_{j}}}\text{ and
}N=\sum\nolimits_{j=1}^{K}N_{r_{j}}$,

where $w_{j}$ is the weight of the $j$-th triplet pre-defined by the knowledge
base (e.g., ConceptNet), $N_{r_{j}}$ is the number of extracted triplets of
the relation $r_{j}$ for the given instance, and $K$ is the total number of
triplets containing the entity for the given instance.

  4. 4.

For each entity, extract the triplet with the highest score.

#### Grounding with the extracted knowledge.

After extracting the triplet with the highest weight per entity in an
instance, we transform each of them into natural language and concatenate them
to the instance. We use “Context:” as a separator between the input and the
triplets. We leverage the templates that transform a relation into free-text
(e.g., IsA to “is a”) from Petroni et al. (2019).

###  3.1 Experiments

We apply our Know approach to NILE, CAGE, and WT5-base, and name them
KnowNILE, KnowCAGE, and KnowWT5-base, respectively.

#### Inconsistencies.

The results in Table 1 show that grounding in commonsense knowledge diminishes
the number of In-NLEs for all models and tasks. The Know models defended
against 58% of the examples attacked by eKnowIA. Also, we observed that, among
the inconsistent examples of Know models, 20% of them on average were newly
introduced instances. Examples that failed to be defended, as well as newly
introduced In-NLEs are provided in Tables 15-16 in Appendix A.7. Successfully
defended examples are provided in Table 4. More successfully defended
examples, non-defended examples, and newly attacked examples can be found in
Tables 13-14 in Appendix A.7.

First, we highlight that a successfully defended example means that our
eKNowIE attack did not find an adversarial instance together with which the
Know model would form a pair of In-NLEs, while our attack did find at least
one such adversarial instance for the original model. Second, we notice that
even when the selected knowledge might not be the exact knowledge needed to
label an instance correctly, the model can still benefit from this additional
knowledge. For example, in the first sample in Table 14 in Appendix A.7, the
most proper knowledge triplet would be {dog, DistinctFrom, bird}. However,
despite the indirect knowledge given,222ConceptNet does not contain {dog,
DistinctFrom, bird}. i.e., {dog, DistinctFrom, cat}, the model is able to
defend the In-NLE by inferring that dogs are different from other animals. To
examine whether the improved consistency of the Know models stems from
knowledge leakage (using the same knowledge triplets in the mitigation method
as in the attack), we calculate the overlap of triplets. On the e-SNLI
dataset, we find that only 0.3% of knowledge triplets are reused for the
attack on the Know models, and no overlap was found for the Cos-E dataset.
This indicates that the leakage is not significant.

#### NLE quality.

To evaluate the quality of generated NLEs, we conducted a human evaluation
using Amazon MTurk, as automatic evaluation metrics only weakly reflect human
judgements Kayser et al. (2021). We follow the setup from Kayser et al.
(2021): we asked annotators (three per instance) to judge whether the
generated NLEs justify the answer with four options: {no, weak no, weak yes,
yes} and calculated the e-ViL score by mapping them to
$\\{0,{1}/{3},{2}/{3},1\\}$, respectively. Details of the human evaluation are
in Appendix A.5. In Table 1, the Know models show similar NLE quality to their
original counterparts, suggesting that our Know method preserves NLE quality
while decreasing inconsistencies. Similar results are observed on the
automatic evaluation of NLEs (see Appendix A.6).

##  4 Related Work

A growing number of works focus on building NLE models in different areas such
as natural language inference (Camburu et al., 2018), question answering
(Narang et al., 2020), visual-textual reasoning (Hendricks et al., 2018;
Kayser et al., 2021; Majumder et al., 2022), medical imaging (Kayser et al.,
2022), self-driving cars (Kim et al., 2018), and offensiveness classification
(Sap et al., 2019). Most commonly, the performance of these models is assessed
only in terms of how plausible the reasons provided by their NLEs are. To our
knowledge, Camburu et al. (2020) is the only work to investigate
inconsistencies in NLEs. We improve their adversarial attack as well as bring
an approach to alleviate inconsistencies. Works have also been conducted to
analyse and make dialogue models generate responses consistent with the
dialogue history Zhang et al. (2018); Welleck et al. (2019); Li et al. (2020).
However, these works are difficult to be applied to NLE models, in part
because they require specific auxiliary datasets, such as pairs of
inconsistent sentences. Other works investigated the logical consistency of a
model’s predictions Elazar et al. (2021); Mitchell et al. (2022); Kumar and
Joshi (2022); Lin and Ng (2022), but would not have straightforward extensions
for investigating NLEs inconsistencies. Besides consistency, NLEs can also be
assessed for their faithfulness w.r.t. the decision-making process of the
model that they aim to explain (Wiegreffe et al., 2021; Atanasova et al.,
2023).

##  5 Summary and Outlook

We proposed the eKnowIA attack, which is more generalizable, successful, and
faster than the previous eIA attack in detecting In-NLEs. Our experiments show
that current NLE models generate a significant number of In-NLEs, and that
higher NLE quality does not necessarily imply fewer inconsistencies. We also
introduced a simple but efficient method that grounds a model into relevant
knowledge, decreasing the number of In-NLEs. Our work paves the way for
further work on detecting and alleviating inconsistencies in NLE models.

## Limitations

Our eKnowIA attack contains logical rules designed specifically for the
English language. While these rules may apply or be adapted to other languages
with simple morphology, there could be languages in which completely new rules
may be needed. Both our attack and the Know method rely on knowledge bases,
which may sometimes be noisy. We employed manual efforts to eliminate (a small
number of) noisy triples from ConceptNet. Our attack also relies on a manual
annotation to ensure that the adversarial inputs are natural (estimated to be
the case 81.5% of the time). Finally, we were not able to test our methods on
instances with long text, as we are not aware of datasets with NLEs for long
text inputs or long NLEs.

## Acknowledgements

This work was partially supported by the Alan Turing Institute under the EPSRC
grant EP/N510129/1, by the AXA Research Fund, and by the EU TAILOR grant
952215. Oana-Maria Camburu was supported by a Leverhulme Early Career
Fellowship. We also acknowledge the use of Oxford’s ARC facility, of the
EPSRC-funded Tier 2 facility JADE 2 (EP/ T022205/1), and of GPU computing
support by Scan Computers International Ltd.

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DNN

    deep neural network
NLP

    natural language processing
PLM

    pre-trained language model
POS

    part-of-speech
NLI

    natural language inference
CQA

    commonsense question answering
AMT

    Amazon Mechanical Turk

##  Appendix A Appendix

###  A.1 Implementation Details

We implemented the WT5-base model based on the HuggingFace transformers
package333https://github.com/huggingface/transformers and replicated
performance close to the reported results (see Section A.3). For the other
models, we used the implementations provided by the respective authors. A
single Titan X GPU was used.

###  A.2 Training RevExpl

We adopted T5-base Raffel et al. (2020) for training the reverse explainer
(RevExpl). We trained the model for 30 epochs with a batch size of 8. For
efficient training, early stopping was applied if the validation loss
increases for 10 consecutive logging steps, which were set to 30,000
iterations. The dropout ratio was set to 0.1. We used the AdamW optimiser
Loshchilov and Hutter (2018) with learning rate $1e^{-4}$ and epsilon
$1e^{-8}$. We also used gradient clipping to a maximum norm of 1.0 and a
linear learning rate schedule decaying from $5e^{-5}$.

For Cos-E, we used 10% of the training data as the validation set, and the
original validation set as the test set.

###  A.3 WT5-base Performance Replication

This section describes the performance of our trained WT5-base model. We
report the accuracy for measuring the performance on the natural language
inference (NLI) and CQA tasks. To automatically evaluate the quality of
generated NLEs, we use the BLEU score Papineni et al. (2002), ROUGE Lin
(2004), Meteor Banerjee and Lavie (2005), and the BERT score Zhang et al.
(2020), which are widely used automatic evaluation metrics. The results are
summarised in Table 5. In terms of accuracy and BLEU score, our replication
performs better than originally reported for Cos-E, but produced slightly
lower results for e-SNLI.

|  | Acc. | BLEU | R-1 | R-2 | R-L | Meteor | BERT-S
---|---|---|---|---|---|---|---|---
e-SNLI | ours | 90.6 | 28.4 | 45.8 | 22.5 | 40.6 | 33.7 | 89.8
reported | 90.9 | 32.4 | - | - | - | - | -
Cos-E | ours | 65.3 | 7.3 | 25.0 | 8.3 | 21.6 | 20.2 | 86.3
reported | 59.4 | 4.6 | - | - | - | - | -
Table 5: Performance of our implementation of WT5-base on e-SNLI and Cos-E.
The notations R-1, R-2, R-L, and BERT-S denote ROUGE-1, ROUGE-2, ROUGE-L
score, and BERT-Score, respectively.

###  A.4 Naturalness Evaluation of the Generated Variable Parts

It could be unfair to consider that a model generates inconsistent NLEs if the
adversarial variable parts are unnatural. Hence, we manually evaluated 50
random samples of generated adversarial variable parts for each model (or all
samples when there were less than 50 pairs of inconsistencies found).

On e-SNLI, we observe that, on average, 81.5% ($\pm$ 1.91) of the reverse
variable parts were natural instances, i.e., semantically valid and not
contradicting commonsense. The specific figures for each e-SNLI model were
80%, 80%, 84%, and 82% for KnowNILE, NILE, WT5, and KnowWT5, respectively. We
adapted the results in Table 1 to reflect the number of inconsistencies caused
only by natural variable parts.

For the Cos-E dataset, we considered that the variable parts (the two
incorrect answer choices) are unnatural if (1) the answer choices are
stopwords of the NLTK package or (2) the correct answer is repeated. We
observed only one unnatural case for KnowWT5 and WT5, respectively, and none
for the other two models. We eliminated the two cases from the counts.

###  A.5 Design of Human Evaluation Process for Assessing NLE Quality

For the human evaluation, we sampled 200 generated NLEs for each model. Three
Anglophone annotators are employed per instance. We selected annotators with a
Lifetime HITs acceptance rate of at least 98% and an accepted number of HITs
greater than 1,000. However, it is widely known that the quality of MTurk
annotation is not guaranteed even for Master workers Rouse (2019). When we
used the e-ViL evaluation framework off-the-shelf (Kayser et al., 2021), we
found that many workers do annotations without due consideration by simply
checking “yes” in most cases. We also initially obtained an inter-annotator
agreement captured by Fleiss’s Kappa ($\mathcal{K}$) of only 0.06 on average
for Cos-E, which casted doubt on the quality of the evaluation. This prompted
us to add a quality control measure to the evaluation framework. We carefully
collected trusted examples where the quality of the NLEs is objectively “yes”
or “no”. For each HIT consisting of 10 examples, we incorporated in random
locations two trusted examples with the correct answers being “yes” and “no”,
respectively. After annotation, we discarded the HITs where the annotators
gave a wrong answer for any of the trusted examples (we consider correct a
“weak yes” answer for a “yes” trusted example and a “weak no” for a “no”
trusted example). We repeated this process until the number of rejected HITs
was fewer than 15% of the total HITs. We achieved an increased $\mathcal{K}$
value of 0.46 and 0.34 for e-SNLI and Cos-E, respectively, from 0.35 and 0.06
(without trusted examples). Similar levels of $\mathcal{K}$ as ours were
obtained in other studies, such as Marasović et al. (2022); Yordanov et al.
(2022).

###  A.6 Quality Evaluation on the Generated NLEs

Table 6 shows the detailed results of human evaluation on the quality of
generated NLEs. In addition to the e-ViL score, we followed the evaluation
method of Marasović et al. (2020) by merging weak no and weak yes to no and
yes, respectively, and reporting the ratios of w/yes and w/no. Also, the
results of the automatic evaluation metrics are provided in Table 7. The
results show that all the Know-models show similar or better results than
their original counterparts.

Model | e-SNLI | Cos-E
---|---|---
e-ViL | W/Yes | W/No | e-ViL | W/Yes | W/No
CAGE | - | - | - | 0.43 | 46 | 54
KnowCAGE | - | - | - | 0.44 | 47 | 53
NILE | 0.80 | 83 | 17 | - | - | -
KnowNILE | 0.82 | 86 | 14 | - | - | -
WT5 | 0.76 | 80 | 20 | 0.55 | 55 | 45
KnowWT5 | 0.80 | 84 | 16 | 0.56 | 57 | 43
Table 6: Human evaluation results on the generated NLEs. The number of “W/Yes” (merged “Yes” and “Weak Yes”) and “W/No” (“No” and “Weak No”) are in %. The best results are in bold. |  | BLEU | R-1 | R-2 | R-L | Meteor | BERT-S
---|---|---|---|---|---|---|---
e-SNLI | WT5-base | 28.4 | 45.8 | 22.5 | 40.6 | 33.7 | 89.8
KnowWT5-base | 30.6 | 48.2 | 24.6 | 43.0 | 38.0 | 90.5
NILE | 22.3 | 41.7 | 18.7 | 36.3 | 30.2 | 90.0
KnowNILE | 22.4 | 42.0 | 18.9 | 36.5 | 30.5 | 90.1
Cos-E | WT5-base | 7.3 | 25.0 | 8.3 | 21.6 | 20.2 | 86.3
KnowWT5-base | 7.9 | 26.7 | 9.6 | 22.9 | 21.8 | 86.7
CAGE | 3.0 | 9.7 | 1.1 | 9.0 | 6.3 | 85.1
KnowCAGE | 3.0 | 9.8 | 1.0 | 9.0 | 6.4 | 85.1
Table 7: Automatic evaluation results on generated NLEs on the e-SNLI and Cos-E datasets. The notations R-1, R-2, R-L, and BERT-S denote ROUGE-1, ROUGE-2, ROUGE-L score, and BERT-Score, respectively. The best results are in bold. Subject | Relation | Object
---|---|---
men | Antonym | humans
man | Antonym | person
woman | Antonym | person
people | Antonym | person
flower | DistinctFrom | plant
politician | Antonym | man
children | Antonym | people
Table 8: List of filtered noisy triplets in ConceptNet.

###  A.7 Examples

Original Explanation |  Reverse Explanation
---|---
Not all men are teaching science. | Not all men are teaching biology.
A dog is not a car. | A dog is not a bike.
The boy is not necessarily looking at another boy. | The boy is not necessarily looking at another female.
A child is not a man. | A child is not a wife.
A bird is not a squirrel. | A bird is not a moose.
A group of dogs is not a woman. | A group of dogs is not a person.
Table 9: Examples where both the original and reverse NLEs contain negation
expressions. These NLEs are not contradictory with each other. Premise: Two
hussars sit perched on horses, dressed in extravagant ceremonial wear, each
holding a sabre in their right hand, reigns to the horse in their left.
---
Hypothesis: There are professional riders at a ceremony. |  Hypothesis: Two amateur riders are riding horses.
Predicted Label: Entailment |  Predicted Label: Entailment
Explanation: Hussars are professional riders. |  Explanation: Hussars are amateur riders.
Premise: A cheerleader in a tight red and white uniform is passing out white
t-shirts at a sporting event.
Hypothesis: A player passes out hotdogs. |  Hypothesis: A player is passing out shirts.
Predicted Label: Contradiction |  Predicted Label: Entailment
Explanation: A cheerleader is not a player. |  Explanation: A cheerleader is a player.
Premise: Two people using a water buffalo to cultivate a watery field.
Hypothesis: Two people are outside with animals. |  Hypothesis: Two people are using a plant.
Predicted Label: Entailment |  Predicted Label: Entailment
Explanation: A water buffalo is an animal. |  Explanation: A water buffalo is a plant.
Question: Crabs live in what sort of environment?
Choices: bodies of water, saltwater, galapagos |  Choices: bodies of earth, saltwater, atlantic ocean
Predicted Answer: bodies of water |  Predicted Answer: bodies of earth
Explanation: Crabs live in bodies of water. |  Explanation: Crabs live in bodies of earth.
Question: The piece of paper was worth a lot of money, it was an old Apple Inc
what?
Choices: stock certificate, copy machine, ream |  Choices: stock certificate, piece of stone, book
Predicted Answer: stock certificate |  Predicted Answer: stock certificate
Explanation: A stock certificate is the only thing that is not a piece of paper. |  Explanation: A stock certificate is the only thing that is a piece of paper.
Question: When a person admits his mistakes, what are they doing?
Choices: act responsibly, learn to swim, feel relieved |  Choices: act responsibly, think critically, act irresponsibly
Predicted Answer: act responsibly |  Predicted Answer: act irresponsibly
Explanation: when a person admits his mistakes, they act responsibly. |  Explanation: when a person admits his mistakes, they act irresponsibly.
Table 10: Examples of inconsistent NLEs detected by eKnowIA for the WT5-base
model on e-SNLI and Cos-E. The first column shows the original variable part
and the second column shows the adversarial one. Premise: These are young
adults who seem to be working together to protect the plants surrounding the
white pole.
---
Hypothesis: The adults are young. |  Hypothesis: The young adults are underdeveloped.
Predicted Label: Entailment |  Predicted Label: Entailment
Explanation: Young adults are young. |  Explanation: Young adults are seniors.
Premise: A young boy with close-cropped hair, wearing a red robe, is holding a
black kettle as someone is about to pour something in it.
Hypothesis: a small boy is holding a kettle that will soon be filled |  Hypothesis: The small boy is not underdeveloped.
Predicted Label: Entailment |  Predicted Label: Entailment
Explanation: A young boy is a small boy. |  Explanation: A young boy is a small female.
Premise: A group of people in a room, most with computers, some raising their
hands.
Hypothesis: Some people have questions. |  Hypothesis: A group of people are asking questions.
Predicted Label: Neutral |  Predicted Label: Contradiction
Explanation: People raising their hands are not asking questions. |  Explanation: People raising their hands are asking questions.
Table 11: Examples of inconsistent NLEs detected by our eKnowIA attack for the
NILE model on e-SNLI. The first column shows the original hypothesis, and the
second one shows the adversarial hypothesis from our attack. Question: A good
interview after applying for a job may cause you to feel what?
---
Choices: hope, income, offer |  Choices: hope, resentment, fear
Predicted Answer: hope |  Reverse Answer: hope
Explanation: hope is the only thing that would cause you to feel hope. |  Explanation: hope is the only thing that would cause you to feel fear.
Question: What does a stove do to the place that it’s in?
Choices: warm room, brown meat, gas or electric |  Choices: warm room, cook food, heat the outside
Predicted Answer: warm room |  Reverse Answer: heat the outside
Explanation: a stove heats the room. |  Explanation: a stove heats the outside.
Table 12: Examples of inconsistent NLEs detected by our eKnowIA attack for the
CAGE model on Cos-E. The first column shows the original hypothesis, and the
second column shows the adversarial hypothesis from our attack. Premise: A dog
standing near snow looking at water.
---
Hypothesis: A bird is standing near snow. |  Hypothesis: A bird is near water.
Predicted Label: contradiction |  Predicted Label: entailment
Explanation: A dog is not a bird. |  Explanation: A dog is a bird.
Question: What is a person who is good at sports considered?
Choices: talented, affluent, reproduce |  Choices: talented, untalented, good at
Predicted Label: talented |  Predicted Label: untalented
Explanation: a person who is good at sports is considered talented. |  Explanation: a person who is good at sports is considered untalented
Table 13: Examples of inconsistent NLEs detected by eKnowIA for NILE on e-SNLI
and WT5 on Cos-E. The first column shows the original variable part, and the
second column shows the adversarial one. Premise: A dog standing near snow
looking at water. Hypothesis: A bird is near water.
---
Hypothesis: A bird is standing near snow. |  Hypothesis: A bird is near water.
Predicted Label: contradiction |  Predicted Label: neutral
Explanation: A dog is not a bird. |  Explanation: A dog looking at water does not imply a bird is near water.
Extracted Knowledge: {snow, RelatedTo, water}, {dog, DistinctFrom, cat} |  Extracted Knowledge: {snow, RelatedTo, water}, {dog, DistinctFrom, cat}
Question: What is a person who is good at sports considered? Choices:
talented, untalented, good at
Choices: talented, affluent, reproduce |  Choices: talented, untalented, good at
Predicted Label: talented |  Predicted Label: talented
Explanation: a person who is good at sports is considered talented. |  Explanation: a person who is good at sports is considered talented.
Extracted Knowledge: {talent, RelatedTo, sports} |  Extracted Knowledge: {talent, RelatedTo, sports}
Table 14: Examples of successfully defended instances by the KnowNILE model on
e-SNLI and by the KnowWT5 model on Cos-E. This table should be read together
with Table 13 to appreciate the defence.  Model: KnowWT5 Premise: A young
family enjoys feeling ocean waves lap at their feet.
---
Original |  Adversarial
Hypothesis: A family is at the beach. |  Hypothesis: The family is not at the beach.
Predicted Label: entailment |  Predicted Label: entailment
Explanation: Ocean waves lap at their feet implies that they are at the beach. |  Explanation: Ocean waves lap at their feet implies that they are not at the beach.
|  Extracted Knowledge: {feet, HasA, fingerprints}
Model: KnowNILE Premise: Some dogs are running on a deserted beach.
Original |  Adversarial
Hypothesis: There are multiple dogs present. |  Hypothesis: There are not many dogs on the beach.
Predicted Label: entailment |  Predicted Label: entailment
Explanation: Some dogs implies multiple dogs. |  Explanation: Some dogs implies not many dogs.
|  Extracted Knowledge: {dog, DistinctFrom, cat}
Model: KnowCAGE Question: What does the sky do before a rain?
Choices: cloud over, rain water, appear blue |  Choices: cloud over, appear blue, appear green
Predicted Label: appear blue |  Predicted Label: appear green
Explanation: the sky appears blue before a rain |  Explanation: the sky appears green before a rain
|  Extracted Knowledge: {sky, UsedFor, rain}
Table 15: Examples of inconsistent NLEs detected by eKnowIA but not defended
by Know-models. The extracted knowledge triplets are not highly related to
generating correct explanations. Model: KnowWT5 Premise: The collie is
standing outdoors on a sandy area.
---
Original |  Adversarial
Hypothesis: The collie is standing in the sand. |  Hypothesis: The collie is standing on stone.
Predicted Label: entailment |  Predicted Label: entailment
Explanation: A sandy area is made of sand. |  Explanation: A sandy area is made of stone.
|  Extracted Knowledge: {sand, RelatedTo, rock}
Model: KnowNILE Premise: Coach talks with football player, other players and
crowd in background.
Original |  Adversarial
Hypothesis: A football player is climbing into the stands at a game. |  Hypothesis: A football player talks to a crowd.
Predicted Label: contradiction |  Predicted Label: entailment
Explanation: A coach is not a football player. |  Explanation: A coach is a football player.
|  Extracted Knowledge: {crowd, IsA, gathering}, {player, PartOf, team},
{football player, DerivedFrom, football}
Table 16: Examples of newly detected instances with inconsistent NLEs by
eKnowIA for the Know models. The extracted knowledge triplets exhibit low
relevance and confuse the model to generate incorrect explanations.

  *[POS]: part-of-speech
  *[CQA]: commonsense question answering
  *[NLI]: natural language inference