# Instructions for *ACL Proceedings

Julia Rozanova1, Marco Valentino2, André Freitas1,2
University of Manchester, United Kingdom1
<EMAIL_ADDRESS>
Idiap Research Institute, Switzerland2
<EMAIL_ADDRESS>

# Estimating the Causal Effects of
Natural Logic Features in Neural NLI Models

Julia Rozanova1, Marco Valentino2, André Freitas1,2
University of Manchester, United Kingdom1
<EMAIL_ADDRESS>
Idiap Research Institute, Switzerland2
<EMAIL_ADDRESS>

###### Abstract

Rigorous evaluation of the causal effects of semantic features on language
model predictions can be hard to achieve for natural language reasoning
problems. However, this is such a desirable form of analysis from both an
interpretability and model evaluation perspective, that it is valuable to zone
in on specific patterns of reasoning with enough structure and regularity to
be able to identify and quantify systematic reasoning failures in widely-used
models. In this vein, we pick a portion of the NLI task for which an explicit
causal diagram can be systematically constructed: in particular, the case
where across two sentences (the premise and hypothesis), two related
words/terms occur in a shared context.

In this work, we apply causal effect estimation strategies to measure the
effect of _context_ interventions (whose effect on the entailment label is
mediated by the semantic _monotonicity_ characteristic) and interventions on
the inserted word-pair (whose effect on the entailment label is mediated by
the _relation_ between these words.). Following related work on causal
analysis of NLP models in different settings, we adapt the methodology for the
NLI task to construct comparative model profiles in terms of _robustness to
irrelevant changes_ and _sensitivity to desired changes_.

##  1 Introduction

There is an abundance of reported cases where high accuracies in NLP tasks can
be attributed to simple heuristics and dataset artifacts McCoy et al. (2019a).
As such, when we expect a language model to capture a specific reasoning
strategy or correctly use certain semantic features, it has become good
practice to perform evaluations that provide a more granular and qualitative
view into model behaviour and efficacy. In particular, there is a trend in
recent work to incorporate causal measures and _interventional_ experimental
setups in order to better understand the captured features and reasoning
mechanisms of NLP models Vig et al. (2020); Finlayson et al. (2021); Stolfo et
al. (2022); Geiger et al. (2021).

Figure 1: We propose a causal intervention framework for systematically
inspecting monotonicity reasoning in NLI models.

In general, it can be hard to pinpoint all the intermediate features and
critical representation elements which are guiding the inference behind an NLP
task. However, in many cases there are subtasks which have enough
semantic/logical regularity to perform stronger analyses and diagnose clear
points of failure within larger tasks such as NLI and QA (Question Answering).
As soon as we are able to draw a causal diagram which captures a portion of
the model’s expected reasoning capabillities, we may be guided in the design
of interventional experiments which allow us to estimate causal quantities of
interest, giving insight into how different aspects of the inputs are used by
models.

In this work, we look at a structured subset of the NLI task Rozanova et al.
(2021b) to investigate the use of two semantic inference features by NLI
models: concept relations and logical monotonicity.

Our workflow is described in figure 1: we use intermediate abstracted semantic
feature labels to construct _intervention sets_ out of NLI examples which
allow us to measure certain causal effects. Building upon recent work on
causal analysis of NLP models Stolfo et al. (2022), we use the ntervention
sets to systematically and quantitatively characterise models’ _sensitivity_
to relevant changes in these semantic features and _robustness_ to irrelevant
changes.

Our contributions may be summarised as follows:

  * •

Following previous work on causal analysis of NLP models, we zone in on a
structured subproblem in NLI (in our case, a natural logic based subtask) and
present a causal diagram which captures both desired and undesired potential
reasoning routes which may describe model behaviour.

  * •

We adapt the NLI-XY dataset of Rozanova et al. (2021b) to a meaningful
collection of _intervention sets_ which enable the computation of certain
causal effects.

  * •

We calculate estimates for undesired direct causal effects and desired total
causal effects, which also serve as a quantification of model robustness and
sensitivity to our intermediate semantic features of interest.

  * •

We compare a suite of mainstream neural NLI models, identifying behavioural
weaknesses in high-performing models and behavioural advantages in some worse-
performing ones.

To the best of our knowledge, we are the first to complement previous
observations of models’ brittleness with respect to context monotonicity with
the evidence of causal effect measures, as well as presenting new insights
that over-reliance on lexical relations is consequently also tempered by the
same improvement strategies.

##  2 Problem Formulation

###  2.1 A Structured NLI Subtask

As soon as we have a concrete description of how a reasoning problem _should_
be treated, we can begin to evaluate how well a model emulates the expected
behaviour and whether it is capturing the semantic abstractions at play.

In this work, we consider an NLI subtask which comes from the broader setting
of _Natural Logic_ MacCartney and Manning (2007); Hu and Moss (2018); Sánchez
(1991). As it has a rigid and well-understood structure, it is often used in
interpretability and explainability studies for NLI models Geiger et al.
(2021); Richardson et al. (2019a); Geiger et al. (2022); Rozanova et al.
(2021b, a). We begin with the format described in Rozanova et al. (2021b) (we
refer to this work for more detailed description and full definitions).

Consider two terms/concepts with a known _relation label_ , such as one of the
pairs:

Word/Term $x$ | Word/Term $y$ | Relation
---|---|---
brown sugar | sugar | $x\sqsubseteq y$
mammal | lion | $x\sqsupseteq y$
computer | pomegranate | $x\\#y$
Table 1: Examples of the word-pair/term-pair relation informing entailment
examples. Concept inclusion and its dual are related by $\sqsubseteq$ and
$\sqsupseteq$ respectively, while $\\#$ denotes the absence of either
relations.

Suppose the two terms occur in an identical context (comprising of a natural
language sentence, like a template), for example:

Premise | I do not have any sugar.
---|---
Hypothesis | I do not have any brown sugar.

A semantic property of the natural language context called _monotonicity_
determines whether there is an _entailment relation_ between the sentences
generated upon substitution/insertion of given related terms (formally, this
is monotonicity in the sense of preserving the “order" between the inserted
terms to an equally-directed entailment relation between the sentences.) The
context monotonicity may either be _upward_ ($\uparrow$) or _downward_
($\downarrow$, as in the example above) or _neither_.

We can describe how the monotonicity behaves in conjunction with the relation
between the inserted words in the causal diagram in figure 2.

Variable | Description
---|---
$G$ | Gold Label
$C$ | Context
$M$ | Context Monotonicity
$W$ | Inserted Word Pair
$R$ | Word-Pair Relation

$C$$M$$W$$R$$G$

Figure 2: Causal Diagram for the Natural Logic Subtask

The way in which the value of these variables affects the gold entailment
label is summarised in table 2.

$M$ $R$ | $\sqsubseteq$ | $\sqsupseteq$ | $\\#$
---|---|---|---
$\uparrow$ | Entailment | Non-Entailment | Non-Entailment
$\downarrow$ | Non-Entailment | Entailment | Non-Entailment

Table 2: The entailment gold labels as a function of two semantic features:
the context montonicity (M) and the relation (R) of the inserted word pair.

The authors of Rozanova et al. (2021b) provide a thus-formatted dataset called
_NLI-XY_ , which we use as the basis for our causal effect estimation
experiments.

Naturally, it is always likely that models may fail to follow the described
reasoning scheme for these NLI problems. In the next section (2.2), we propose
a causal diagram which also captures the reasoning possibilities an NLI model
may follow, accounting for possible confounding heuristics via unwanted direct
effects.

###  2.2 The Causal Structure of Model Decision-Making

In an ideal situation, a strong NLI model would identify the word-pair
relation and the context monotonicity as the abstract variables relevant to
the final entailment label. In this case, these features would causally affect
the model prediction in the same way they affect the gold label.
Realistically, as shown in illuminating studies such as McCoy et al. (2019b),
models identify unexpected biases in the dataset and may end up using
accidental correlations output labels, such as the frequency of certain words
in a corpus. For example, McCoy and Linzen (2018) demonstrate how models can
successfully exploit the presence of negation markers to anticipate non-
entailment, even when it is not semantically relevant to the output label.

To ensure that the semantic features themselves are taken account into the
model’s output and not other surface-level confounding variables, one would
like to perform interventional studies which alter the value of the target
feature but not other confounding variables. This is, in many cases, not
feasible (although attempts are sometimes made to at least perform
interventions that only make minimal changes to the surface form, as in
Kaushik et al. (2020).)

Stolfo et al. (2022) argue that it is useful to quantify instead the direct
impact of irrelevant surface changes (controlling for values of semantic
variables of interest) and compare them to _total causal effects_ of input-
level changes: doing so, we may posit deductions about the flow of information
via the semantic variables (or lack thereof). For analyses where there is an
attempt to align intermediate variables with explicit internals, see vig-
gender, Finlayson et al. (2021) for a mediation analysis approach, or Geiger
et al. (2021) for an alignment strategy based on causal abstraction theory.

#### Diagram Specification

We follow Stolfo et al. (2022) in the strategy of explicitly modeling the
“irrelevant surface form" of the input text portions as variables in the
causal diagram. Their setting of _math word problems_ is decomposed into two
compositional inputs: a question template and two integer arguments. Our
setting follows much the same structure: our natural language “context” plays
the same role as their “template”, but our arguments (an inserted word pair)
have an additional layer of complexity as we also model the _relation_ between
the arguments as an intermediate reasoning variable rather than the values
themselves (as such, the structure of their template modeling in their causal
diagram is more applicable than the direct way they treat their numerical
arguments.)

Variable | Description
---|---
$Y$ | Model Prediction
$G$ | Gold Label
$C$ | Context
$M$ | Context Monotonicity
$S_{C}$ | Context Textual Surface Form
$W$ | Inserted Word Pair
$R$ | Word-Pair Relation
$S_{W}$ | Word-Pair Textual Surface Form

$C$$S_{C}$$M$$W$$S_{W}$$R$$G$$Y$

Figure 3: Specification of the causal diagram for possible routes of model
reasoning for NLI-XY problems. Green edges indicate _desired_ causal
influence, while red edges indicate _undesired_ paths of causal influence via
surface-level heuristics.

We present our own causal diagram in figure 3. We introduce the textual
context $C$ as an input variable, which is further decomposed into more
abstract variables: its _monotonicity_ $M$ (which directly affects the gold
truth $G$) and the textual surface form of the context $S_{C}$. The other
input variable is the word-pair insertion which we will summarise as a single
variable $W$. Once again, $W$ has a potential effect on the model decision
through its surface form $S_{W}$ and the via relation $R$ between the words.
The gold truth $G$ is dependant on $W$ and $R$ only. Finally, the outcome
variable is the model prediction $Y$. The paths for which we would like to
observe the highest causal effect are the paths to $Y$ from the inputs via
$M,R$ and through the gold truth variable $G$. However, each of
$S_{C},S_{W},M,R,C$ and $W$ have direct links to the model output $Y$ as well
(indicated in red): these are potential direct effects which are _unwanted_.
The key goal of this study is to compare the extent to which models exhibit
the high causal effects for the _desired_ diagram routes and lower causal
effects for the _undesired_ routes.

##  3 Estimating the Causal Effects

Causal effects can be calculated via the implementation of interventional
experiments. The calculation strategy for the causal effect of a given
_source_ variable on a _target_ variable depends on the structure of the
causal diagram, and subsequently the set of interventions and controls that
need to be performed in order to estimate the causal influence in question.
Only certain interventions are feasible in our setting, so we mention right
off the bat that most of the causal effects that are of greatest interest will
not be possible to estimate. However, as with the work in Stolfo et al.
(2022), there are enough useful interventions we can perform (and thus causal
effects we can measure) to make useful model comparisons and develop some
insights into models’ robustness and sensitivity to distinct classes of of
input changes.

For the algebra of causal effect calculation, we draw heavily on the reasoning
in Stolfo et al. (2022), as the upper portion of their causal diagram
(concerning the natural language question template, its surface form and the
implicit math operation) is equivalent to both the upper and lower half of our
diagram in figure 3.

We start with the _Total Causal Effect_ (TCE) of the respective textual inputs
$C$ (the context) and $W$ (the inserted word pair).

#### Total Causal Effects

To obtain the total causal effect of the context $C$, we intervene on the
context _without conditioning on the context monotonicity._ It can calculated
as

$\displaystyle\mbox{TCE}(C~{}\mbox{on }Y)$
$\displaystyle:=\mathbb{E}^{\textrm{int}+}_{C}[Y]-\mathbb{E}^{\textrm{int}-}_{C}[Y],$
(1)

where $\mathbb{E}^{\textrm{int}+}_{C}[Y]$ indicates the expected prediction
value _after_ intervention on $C$, while $\mathbb{E}^{\textrm{int}-}_{C}[Y]$
denotes the expected outcome _before_ this intervention.

On the other hand, to obtain the total causal effect of the inserted word pair
$W$, we intervene on the word pair _without conditioning on the context
monotonicity._ It can calculated as

$\displaystyle\mbox{TCE}(W~{}\mbox{on }Y)$
$\displaystyle:=\mathbb{E}^{\textrm{int}+}_{W}[Y]-\mathbb{E}^{\textrm{int}-}_{W}[Y].$
(2)

This is a quantification of their respective causal influence on the outcome
$Y$ _through all possible paths_ : in particular, this captures both the
undesired route of influence (via surface heuristics) and the desired route
(via the corresponding semantic feature and gold truth label).

Target Quantity | Intervention |  Premise |  Hypothesis | M | R | G
---|---|---|---|---|---|---
TCE($W\to Y$) | Before |  There’s a cat on the pc. |  There’s a cat on the machine. | $\uparrow$ | $\sqsubseteq$ | Entailment
After |  There’s a cat on the tree. |  There’s a cat on the fruit tree. | $\uparrow$ | $\sqsupseteq$ | Non-Entailment
DCE($S_{W}\to Y$) | Before |  There are no students yet. |  There are no first-year students yet. | $\downarrow$ | $\sqsupseteq$ | Entailment
After |  There are no people yet. |  There are no women yet. | $\downarrow$ | $\sqsupseteq$ | Entailment

Table 3: Example word-pair insertion interventions for determining the total
causal effect of label-relevant word-pair changes and the direct causal effect
of label-irrelevant word-pair changes.

Target Quantity | Intervention |  Premise |  Hypothesis | M | R | G
---|---|---|---|---|---|---
TCE($C\to Y$) | Before |  You can’t live without fruit . |  You can’t live without strawberries . | $\uparrow$ | $\sqsupseteq$ | Non-Entailment
After |  All fruit study english. |  All strawberries study English. | $\downarrow$ | $\sqsupseteq$ | Entailment
DCE($S_{C}\to Y$) | Before |  He has no interest in seafood . |  He has no interest in oysters . | $\downarrow$ | $\sqsupseteq$ | Entailment
After |  I don’t want to argue about this in front of seafood . |  I don’t want to argue about this in front of oysters . | $\downarrow$ | $\sqsupseteq$ | Entailment

Table 4: Example context interventions for determining the total causal effect
of label-relevant context changes and the direct causal effect of label-
irrelevant context changes.

#### Undesired Direct Causal Effects

The key suggestion in Stolfo et al. (2022) is that even though we have no
feasible intervention strategies which allow us to calculate the direct causal
effect (DCE) of the intermediate variable $M$ (respectively, $R$), we may
yield some insight into their causal influence by comparing the relevant TCE
to the _unwanted direct effect_ DCE ($S_{C}\to Y$) (respectively, DCE
($S_{W}\to Y$)).

In principle, this direct effect can be seen as _accounting for a portion of
the total causal influence_ which is indicated by the TCE. More importantly,
this quantity can serve as an estimate of _robustness_ to irrelevant changes
in the given input variable. We calculate it by conditioning on the
corresponding semantic feature variables of interest (note that for these
interventions, the gold label is unchanged as it is dependant on the variables
we are conditioning for):

$\begin{split}&\mbox{DCE}(S_{C}~{}\mbox{on }Y):=\\\
&\sum_{m}P(M)(\mathbb{E}^{\textrm{int}+}_{S_{C}}[Y\mid
M=m]-\mathbb{E}^{\textrm{int}-}_{S_{C}}[Y\mid M=m])\\\ \end{split}$ (3)
$\begin{split}&\mbox{DCE}(S_{W}~{}\mbox{on }Y):=\\\
&\sum_{r}P(R)(\mathbb{E}^{\textrm{int}+}_{S_{W}}[Y\mid
R=r]-\mathbb{E}^{\textrm{int}-}_{S_{W}}[Y\mid R=r]).\\\ \end{split}$ (4)

##  4 Experimental Setup

###  4.1 Data and Interventions

Whether we are measuring a DCE or TCE differs mostly in the choice of
interventions. As such, the core of this work is compiling the relevant data
into meaningful sets of interventions. To this end, let $N$ denote an NLI-XY
example consisting of a tuple $(P,H,C,W,M,R,G)$. If we allow that $W$ is a
pair $(w_{1},w_{2})$, the premise $P$ and hypothesis $H$ are substitutions
into the context $C$, so that $P=C(w_{1})$, $H=C(w_{2})$. The gold label $G$
is determined as by table 2 depending on the values of $M$ and $R$.

We define an _intervention_ as a pair $(N,N^{\prime})$ of NLI-XY examples,
which will be subject to certain conditioning restrictions depending on the
causal effect quantity we wish to determine. These conditioning restrictions
are summarized by the _intervention schema_ in table 5.

Target Measure | $C$ | $W$ | $M$ | $R$ | $G$ |  |  Interventions in Dataset
---|---|---|---|---|---|---|---
DCE $(S_{C}\to Y)$ | $\not=$ | $=$ | $=$ | $=$ | $=$ |  |  20910
TCE $(C\to Y)$ | $\not=$ | $=$ | $\not=$ | $=$ | $\not=$ |  |  14270
DCE $(S_{W}\to Y)$ | $=$ | $\not=$ | $=$ | $=$ | $=$ |  |  25960
TCE $(W\to Y)$ | $=$ | $\not=$ | $=$ | $\not=$ | $\not=$ |  |  22640

Table 5: Intervention schema and dataset statistics: which variables are held
constant and which are changed in the construction of intervention sets for
the calculation of the indicated effects.

We use the NLI-XY evaluation dataset to construct intervention pairs
$(N,N^{\prime})$ by using a sampling/filtering strategy as in Stolfo et al.
(2022) according to the intervention schema in table 5. In particular, for
constructing _context_ interventions, we sample a seed set of 400 NLI-XY
premise/hypothesis pairs. This is the _pre-intervention_ NLI example. For
each, we fix the insertion pair and filter through the NL-XY dataset for all
examples with the shared insertion pair but different context, conditioned as
necessary on the properties of the other variables as in the intervention
schema. For insertion pairs, we do the opposite. The number of interventions
we produce in this way for our experiments are reflected in the last column of
table 5

In summary, the changes are context replacements and related word-pair
replacements; we provide text-level examples in tables 3 and 4.

###  4.2 Model Choice and Benchmark Comparison

We include the following models 111All pretrained models are from the
Huggingface _transformers_ library (Wolf et al. (2020)), except for infobert
and the pretrained model counterparts fine-tuned on HELP: their sources are
linked in the README of the accompanying code. in our study:

  * •

The models evaluated in NLI-XY paper Rozanova et al. (2021b), namely roberta-
large-mnli, facebook/bart-large-mnli, bert-base-uncased-snli and their
counterparts fine-tuned on the HELP dataset Yanaka et al. (2019b)

  * •

The infobert model, which is trained on three benchmark training sets of
interest: MNLI Williams et al. (2018), SNLI Bowman et al. (2015) and ANLI Nie
et al. (2020) (currently at the top of the leaderboard for the adversarial
ANLI test set, as of January 2023)

  * •

Another roberta-large checkpoint, also trained on all three benchmark NLI
training training sets (as well as FEVER-NLI Nie et al. (2019)).

We report their scores on the mentioned benchmark datasets alongside the
relevant total and direct causal effects we are interested in.

Note that as the HELP dataset is a two class entailment dataset (as opposed to
datasets like MNLI, which are three class), we cannot directly compare
existing reported scores. As such, we adapt the three class scores to a two
class score by grouping two of the three class labels (“contradiction" and
“neutral") into the two class umbrella label "non-entailment". For all models,
we report both the three class and adapted two class accuracy scores on the
benchmark datasets.

##  5 Results and Discussion

We examine and compare the results for the models listed in 4.2. We first zoom
in on the word-pair insertion intervention experiments in 5.1, then the
context intervention experiments in 5.2 and finally present a categorical
overview of these results in section 5.4, contextualised by benchmark scores.

###  5.1 Causal Effect of Inserted Word Pairs

The results for the substituted word-pair intervention experiment are reported
in figure 4. The most desireable outcome is a DCE($S_{C}\to Y$) which is _as
low as possible_ in combination with a TCE($C\to Y$) which is _as high as
possible_. The lower this DCE, the higher the model robustness to _irrelevant
context surface form_ changes. On the other hand, the higher the specified
TCE, the greater the model’s sensitivity to _context changes affecting the
gold label_.

Model |  | DCE($S_{W}\to Y$) | TCE($W\to Y$) |  | TCE/DCE Ratio | Delta
---|---|---|---|---|---|---
bert-base-uncased-snli |  | 0.341 | 0.350 |  | 1.027 | 0.009
bert-base-uncased-snli-help |  | 0.332 | 0.361 |  | 1.087 | 0.029
roberta-large-mnli |  | 0.343 | 0.613 |  | 1.785 | 0.269
roberta-large-mnli-help |  | 0.276 | 0.754 |  | 2.730 | 0.478
facebook/bart-large-mnli |  | 0.342 | 0.618 |  | 1.805 | 0.275
facebook/bart-large-mnli-help |  | 0.268 | 0.766 |  | 2.863 | 0.499
roberta-large-snli_mnli_fever_anli_R1_R2_R3 |  | 0.294 | 0.682 |  | 2.321 | 0.388
infobert |  | 0.291 | 0.674 |  | 2.320 | 0.384

Figure 4: Results for Insertion Interventions

The largest delta between these two quantities can be seen in the roberta-
large-mnli-help and facebook-bart-large-mnli-help models. This is important to
note: the HELP dataset Yanaka et al. (2019b) is explicitly designed to bolster
model success on natural logic problems, but until now there has been little
to no evidence that it improves the treatment of word-pair relations. In
particular, the internal probing results in Rozanova et al. (2021b) show that
probing performance for the intermediate word-pair relation label decreases
slightly for roberta-large-mnli after fine-tuning on HELP; as such, it was
thought that the HELP improvements on natural logic could solely be attributed
to improved context monotonicity treatment. Now, however, we observe distinct
improvements in robustness to irrelevant word-pair insertion changes and
sensitivity to relevant ones.

More generally, the work in Rozanova et al. (2021b) does indicate that the
large MNLI-based models are already very successful in distinguishing the
relation between substituted words. The word-pair relation label has a high
_probing_ result for all of these models, as well as strong signs of
systematicity in their error analysis. This is in line with our observations
of relatively large deltas between the DCE and TCE here, compared to the
smaller BERT-based models.

###  5.2 Causal Effect of Contexts

The results for the context intervention experiments are reported in figure 5.
The most desireable outcome is a DCE($S_{C}\to Y$) which is _as low as
possible_ in combination with a TCE($C\to Y$) which is _as high as possible_.

Model |  | DCE($S_{C}\to Y$) | TCE($C\to Y$) |  | TCE/DCE Ratio | Delta
---|---|---|---|---|---|---
bert-base-uncased-snli |  | 0.412 | 0.468 |  | 1.136 | 0.056
bert-base-uncased-snli-help |  | 0.406 | 0.485 |  | 1.194 | 0.079
roberta-large-mnli |  | 0.107 | 0.081 |  | 0.751 | -0.027
roberta-large-mnli-help |  | 0.163 | 0.828 |  | 5.070 | 0.665
facebook/bart-large-mnli |  | 0.136 | 0.130 |  | 0.954 | -0.006
facebook/bart-large-mnli-help |  | 0.189 | 0.791 |  | 4.167 | 0.601
roberta-large-snli_mnli_fever_anli_R1_R2_R3 |  | 0.093 | 0.093 |  | 1.008 | 0.001
infobert |  | 0.127 | 0.176 |  | 1.385 | 0.049

Figure 5: Results for Context Interventions

For context interventions, we start to see major distinctions in the
sensitivity of models to important context changes - especially the effect of
the HELP fine-tuning dataset in increasing model reasoning with respect to
context structure. In line with previous behavioural findings in Richardson et
al. (2019a); Yanaka et al. (2019b, a); geiger; Rozanova et al. (2021b) and all
the way back to Wang et al. (2018), which observe systematic failure of large
language models in downward monotone contexts, we notice that all of the
models trained only on the large benchmarks sets fail to correctly change
their prediction when a context change requires it to do so (as indicated by
the low TCE score).

In Yanaka et al. (2019b), Rozanova et al. (2021b) and Rozanova et al. (2021a),
the positive effect of the HELP dataset is already evident, but here we may
also compare it to roberta-large-mnli tuned on many additional training sets,
precluding the possibility that its helpfulness can be attributed only to a
“larger amount of training data".

We note that although the situation of the TCE/DCE ratio for roberta-large-
mnli being less than one may seem peculiar, it is important to keep in mind
that the intervention sets used for estimating these quantities are sampled
independently so some margin of error is warranted. As in Stolfo et al.
(2022), we interpret this result to simply mean that the causal influence is
comparable whether we are affecting the ground truth result (as in the
TCE($C\to Y$) case) or not (as in the DCE($S_{C}\to Y$) case).

###  5.3 Other Potential Direct Effects

Note that there are some unwanted direct links whose causal influence we have
not estimated in this work: in particular, the direct effects of the context
monotonicity $M$ and the word-pair relation $R$ that do not factor through the
entailment gold label $G$. Such an instance is easy to imagine - for example,
a model may be biased to conclude that any instance of a correctly identified
$\sqsubseteq$ related word pair across the example (hyponym in the premise,
hypernym in the hypothesis) should be labelled for entailment (regardless of
the context, shared or not). In fact, this is indeed what we hypothesize the
more context-monotonicity-ignorant models (such as roberta-large-mnli) are
doing.

###  5.4 Benchmark Scores and Causal Effects

Model |  | NLI Benchmark Evaluation (2 Class Accuracy) |  | Context Changes |  | Inserted Word-Pair Changes |
---|---|---|---|---|---|---|---
| SNLI | MNLI-M | MNLI-MM | ANLI-R1 | ANLI-R2 | ANLI-R3 |  | Robustness | Sensitivity |  | Robustness | Sensitivity |
bert-base-uncased-snli |  | 0.766 | 0.620 | 0.623 | 0.567 | 0.596 | 0.580 |  | Mid | Mid |  | Mid | Low |
bert-base-uncased-snli-help |  | 0.757 | 0.627 | 0.626 | 0.505 | 0.508 | 0.546 |  | Mid | Mid |  | Mid | Low |
facebook/bart-large-mnli |  | 0.935 | 0.940 | 0.939 | 0.596 | 0.563 | 0.593 |  | High | Low |  | Mid | Mid |
facebook/bart-large-mnli-help |  | 0.727 | 0.802 | 0.795 | 0.538 | 0.489 | 0.528 |  | Mid/High | Highest |  | Highest | Highest |
roberta-large-mnli |  | 0.931 | 0.941 | 0.940 | 0.614 | 0.529 | 0.5325 |  | Highest | Lowest |  | Mid | Mid |
roberta-large-mnli-help |  | 0.738 | 0.668 | 0.656 | 0.565 | 0.554 | 0.574 |  | High | Highest |  | Highest | Highest |
roberta-large-snli_mnli_fever_anli |  | 0.949 | 0.936 | 0.939 | 0.810 | 0.659 | 0.666 |  | Highest | Lowest |  | Mid | Mid/High |
infobert |  | 0.950 | 0.943 | 0.941 | 0.837 | 0.682 | 0.683 |  | High | Low |  | Mid | Mid/High |

Table 6: Overall 2 class accuracy on original NLI benchmarks and qualitative
comparison against the performed causal intervention analysis. The accuracy is
not necessarily predictive of the performances achieved using a systematic
causal inspection.

A summary of the performance of all models on popular benchmarks alongside a
categorical breakdown of robustness and sensitivity is presented in table 6.
The robustness/sensitivity categories are a qualitative assessment,
identifying the _lowest_ and _highest_ scores within a category, and
categorising other models correspondingly as _low_ , _mid_ or _high_
performers for the given categories. The sensitivity property is tied to the
desired total causal effect, while the robustness property is tied to the
undesired direct causal effect (note in particular that the latter is judged
as _inversely proportional:_ the model with the lowest given DCE is judged the
“highest" in terms of robustness).

The key observation is that the models which achieve the highest performance
on benchmarks may be far from the best performers with respect to our
quantitative markers of strong reliance of important causal features. In
particular, models such as _infobert_ are outperformed in our behavioural
causal effect analyses by weaker models that are fine-tuned on a relatively
small helper dataset such as _HELP_. It is important to note that such changes
coincide with drops in benchmarks performance too, but any model interventions
that discourage the exploitation of heuristics (evident from a lower DCE for
surface form features) may have that effect.

##  6 Related Work

#### Natural Logic Handling in NLI Models

It has been known for some time that large NLI models are frequently tripped
up by downward-monotone reasoning Richardson et al. (2019b); Wang et al.
(2018); Yanaka et al. (2019b); Rozanova et al. (2021b); Geiger et al. (2020).
Various datasets have been created to evaluate and improve this behaviour,
such as HELP Yanaka et al. (2019b), MoNLI Geiger et al. (2020), MQNLI Geiger
et al. (2019), MED Yanaka et al. (2019a). Rozanova et al. (2021b) introduced
NLI-XY, secondary compositional dataset built from portions of MED, where the
intermediate features of _context monotonicity_ and _concept relations_ are
explicitly labelled: this is the dataset we use in this work.

Non-causal structural analyses of model internals with respect to natural
logic features include Rozanova et al. (2021b) (a probing study), but we leave
to the next section some existing works where natural logic intersects with
the world of causal approaches to NLP.

#### Causal Analysis in NLP

Causal modelling has appeared in NLP works in various forms, such as the
investigations of the causal influence of data statistics Elazar et al. (2022)
and mediation analyses Vig et al. (2020); Finlayson et al. (2021) which link
intermediate linguistic/semantic features to model internals. Stolfo et al.
(2022), our core reference, appears to be the first to use explicitly causal
effect measures as indicators of sensitivity and robustness (for some non-
causal approaches to measuring model robustness in NLP, we point to Jin et al.
(2019) and Ribeiro et al. (2020)). For a fuller summary of the use of
causality in NLP, please see the survey by Feder et al. (2022).

Specific to natural logic, works with causal approaches include Geiger et al.
(2020) (which perform interchange interventions at a token representation
level), Geiger et al. (2021) (where an ambitious causal abstraction experiment
attempts to align model internals with candidate causal models) and the works
of Geiger et al. (2020) and Wu et al. (2022), (where attempts are made to
build a prescribed causal structure into models themselves). In particular, Wu
et al. (2022) create a “causal proxy model" which becomes the basis for a new
explainable predictor designed to replace the original neural network.

##  7 Conclusion

The results here strongly bolster the fact that similar benchmark accuracy
scores may be observed for models that exhibit very different behaviour,
especially with respect to specific semantic reasoning patterns and higher-
level properties such as robustness/sensitivity with respect to target
features. In this work, we have been able to explicitly observe previously
suspected biases in certain large NLI models. For example, previous
observations Rozanova et al. (2021b); Yanaka et al. (2019a) that roberta-
large-mnli is biased in favour of assuming upward-monotone contexts, ignoring
the effects of things like negation markers, agrees with our observations that
it exhibits poor context sensitivity (a low TCE influence of contexts which
should be changing the output label). Furthermore, the causal flavour of the
study adds a complimentary narrative to works that investigate model internals
via probing Rozanova et al. (2021b) and observe the presence/absence of
intermediate semantic features in model representations. Instead of merely
suggesting that these features are captured, we are able to gain insight into
their causal influence via connected causal effect estimates. The causal
measures presented here show us that even the highest-performing models
systematically show a failure to adapt their predictions to changing context
structure, suggesting an over-reliance on word relations across the premise
and hypothesis. Finally, we have also added a new observation: that strategies
shown to improve responsiveness to these context changes also end up
increasing the _robustness_ to entailment-preserving word-pair insertion
changes.

##  8 Limitations

Pretrained NLI models often differ in their labelling schemes (among
themselves, and from dataset labelling schemes). We have to the best of our
ability attempted to cross-check the correctness of label configurations used,
but there is always the possibility in this case, and we encourage
reproduction and checking of the results before reporting them in any external
works.

The causal modeling in section 2.2 draws heavily on the work in Stolfo et al.
(2022), where there is a small amount of inconsistency between the textual
description of their modelling strategy and the labels in their diagrams. We
follow the version in the textual description rather than splitting the
template input variable into two further text variables - the “relevant” and
“irrelevant” portion of the text - as such a diagram scheme would require
somewhat different interventions than the ones performed. We place some trust
in the authors’ derivations of the intervention schemes for the target causal
effect measures, as we are not causal modelling experts. We also point out
that the causal effect measures presented here are sample-specific estimates,
which could potentially be improved upon with even larger samples of data.

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