SlowGuess/ACL_2020_OCR huggingface dataset:
Are Natural Language Inference Models IMPRESSIVE? Learning Implicature and PRESupposition
Paloma Jeretic\*, Alex Warstadt\*, Suvrat Bhooshan\*, Adina Williams\*
$^{1}$ Department of Linguistics, New York University
$^{2}$ Facebook AI Research
{paloma, warstadt}@nyu.edu, {sbh, adinawilliams}@fb.com
Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer whether a sentence entails another. However, the ability of NLI models to make pragmatic inferences remains understudied. We create an IMPlicature and PRESupposition diagnostic dataset (IMPPRES), consisting of  $>25k$  semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. We use IMPPRES to evaluate whether BERT, InferSent, and BOW NLI models trained on MultiNLI (Williams et al., 2018) learn to make pragmatic inferences. Although MultiNLI appears to contain very few pairs illustrating these inference types, we find that BERT learns to draw pragmatic inferences. It reliably treats scalar implicatures triggered by "some" as entailments. For some presupposition triggers like only, BERT reliably recognizes the presupposition as an entailment, even when the trigger is embedded under an entailment canceling operator like negation. BOW and InferSent show weaker evidence of pragmatic reasoning. We conclude that NLI training encourages models to learn some, but not all, pragmatic inferences.
One of the most foundational semantic discoveries is that systematic rules govern the inferential relationships between pairs of natural language sentences (Aristotle, De Interpretatione, Ch. 6). In natural language processing, Natural Language Inference (NLI)—a task whereby a system determines whether a pair of sentences instantiates in an entailment, a contradiction, or a neutral relation—has been useful for training and evaluating models on sentential reasoning. However, linguists and philosophers now recognize that there
Figure 1: Illustration of key properties of classical entailments, implicatures, and presuppositions. Solid arrows indicate valid commonsense entailments, and arrows with X's indicate lack of entailment. Dashed arrows indicate follow up statements with the addition of in fact, which can either be acceptable (marked with 'X') or unacceptable (marked with '✓').
are separate semantic and pragmatic modes of reasoning (Grice, 1975; Clark, 1996; Beaver, 1997; Horn and Ward, 2004; Potts, 2015), and it is not clear which of these modes, if either, NLI models learn. We investigate two pragmatic inference types that are known to differ from classical entailment: scalar implicatures and presuppositions. As shown in Figure 1, implicatures differ from entailments in that they can be denied, and presuppositions differ from entailments in that they are not canceled when placed in entailment-cancelling environments (e.g., negation, questions).
To enable research into the relationship between NLI and pragmatic reasoning, we introduce IMPPRES, a fine-grained NLI-style diagnostic test dataset for probing how well NLI models perform implicature and presupposition. Containing 25.5K sentence pairs illustrating key properties of these pragmatic inference types, IMPPRES is automatically generated according to linguist-crafted templates, allowing us to create a large, lexically varied, and well controlled dataset targeting specific
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8690-8705 July 5 - 10, 2020. ©2020 Association for Computational Linguistics
instances of both types.
We first investigate whether presuppositions and implicatures are present in NLI models' training data. We take MultiNLI (Williams et al., 2018) as a case study, and find it has few instances of pragmatic inference, and almost none that arise from specific lexical triggers (see §4). Given this, we ask whether training on MultiNLI is sufficient for models to generalize about these largely absent commonsense reasoning types. We find that generalization is possible: the BERT NLI model shows evidence of pragmatic reasoning when tested on the implicature from some to not all, and the presuppositions of certain triggers (only, cleft existence, possessive existence, questions). We obtain some negative results, that suggest that models like BERT still lack a sophisticated enough understanding of the meanings of the lexical triggers for implicature and presupposition (e.g., BERT treats several word pairs as synonyms, e.g., most notably, or and and).
Our contributions are: (i) we provide a new diagnostic test set to probe for pragmatic inferences, complete with linguistic controls, (ii) to our knowledge, we present the first work evaluating deep NLI models on specific pragmatic inferences, and (iii) we show that BERT models can perform some types of pragmatic reasoning very well, even when trained on NLI data containing very few explicit examples of pragmatic reasoning. We publicly release all IMPPRES data, models evaluated, annotations of MultiNLI, and the scripts used to process data.
2 Background: Pragmatic Inference
We take pragmatic inference to be a relation between two sentences relying on the utterance context and the conversational goals of interlocutors. Pragmatic inference contrasts with semantic entailment, which instead captures the logical relationship between isolated sentence meanings (Grice, 1975; Stalnaker, 1974). We present implicature and presupposition inferences below.
2.1 Implicature
Broadly speaking, implicatures contrast with entailments in that they are inferences suggested by the speaker's utterance, but not included in its literal (Grice, 1975). Although there are many types
Type	Example
Trigger	Jo's cat yawned.
Presupposition	Jo has a cat.
Negated Trigger	Jo's cat didn't yawn.
Modal Trigger	It's possible that Jo's cat yawned.
Interrog. Trigger	Did Jo's cat yawn?
Cond. Trigger	If Jo's cat yawned, it's OK.
Negated Prsp.	Jo doesn't have a cat.
Neutral Prsp.	Amy has a cat.
Table 1: Sample generated presupposition paradigm. Examples adapted from the 'change-of-state' dataset.
of implicatures we focus here on scalar implicatures. Scalar implicatures are inferences, often optional, $^{2}$  which can be drawn when one member of a memorized lexical scale (e.g.,  $\langle \text{some}, \text{all} \rangle$ ) is uttered (see §6.1). For example, when someone utters Jo ate some of the cake, they suggest that Jo didn't eat all of the cake, (see Figure 1 for more examples). According to Neo-Gricean pragmatic theory (Horn, 1989; Levinson, 2000), the inference Jo didn't eat all of the cake arises because some has a more informative lexical alternative all that could have been uttered instead. We expect the speaker to make the most informative true statement: $^{3}$  as a result, the listener should infer that a stronger statement, where some is replaced by all, is false.
Implicatures differ from entailments (and, as we will see, presuppositions; see Figure 1) in that they are deniable, i.e., they can be explicitly negated without resulting in a contradiction. For example, someone can utter Jo ate some of the cake, followed by In fact, Jo ate all of it. In this case, the implicature (i.e., Jo didn't eat all the cake from above) has been denied. We thus distinguish implicated meaning from literal, or logical, meaning.
2.2 Presupposition
Presuppositions of a sentence are facts that the speaker takes for granted when uttering a sentence (Stalnaker, 1974; Beaver, 1997). Presuppositions are generally associated with the presence of certain expressions, known as presupposition triggers. For example, in Figure 1, the definite de
<sup>2</sup>Implicature computation can depend on the cooperativity of the speakers, or on any aspect of the context of utterance (lexical, syntactic, semantic/pragmatic, discourse). See Degen (2015) for a study of the high variability of implicature computation, and the factors responsible for it.
3This follows if we assume that speakers are cooperative (Grice, 1975) and knowledgeable (Gazdar, 1979).
$^{1}$ github.com/facebookresearch/ImpPres
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scription the cake triggers the presupposition that there is a cake (Russell, 1905). Other examples of presupposition triggers are shown in Table 1.
Presuppositions differ from other inference types in that they generally project out of operators like questions and negation, meaning that they remain valid inferences even when embedded under these operators (Karttunen, 1973). The inference that there is a cake survives even when the presupposition trigger is in a question (Did Jordan eat some of the cake?), as shown in Figure 1. However, in questions, classical entailments and implicatures disappear. Table 1 provides examples of triggers projecting out of several entailment canceling operators: negation, modals, interrogatives, and conditionals.
It is necessary to clarify in what sense presupposition is a pragmatic inference. There is no consensus on whether presuppositions should be considered part of the semantic content of expressions (see Stalnaker, 1974; Heim, 1983, for opposing views). However, presuppositions may come to be inferred via accommodation, a pragmatic process by which a listener infers the truth of some new fact based on its being presupposed by the speaker (Lewis, 1979). For instance, if Jordan tells Harper that the King of Sweden wears glasses, and Harper did not previously know that Sweden has a king, they would learn this fact by accommodation. With respect to NLI, any presupposition in the premise (short of world knowledge) will be new information, and therefore accommodation is necessary to recognize it as entailed.
NLI has been framed as a commonsense reasoning task (Dagan et al., 2006; Manning, 2006). One early formulation of NLI defines "entailment" as holding for sentences  $p$  and  $h$  whenever, "typically, a human reading  $p$  would infer that  $h$  is most likely true...[given] common human understanding of language [and] common background knowledge" (Dagan et al., 2006). Although this sparked debate regarding the terms inference and entailment—and whether an adequate notion of "inference" could be defined (Zaenen et al., 2005; Manning, 2006; Crouch et al., 2006)—in recent work, a commonsense formulation of "inference" is widely adopted (Bowman et al., 2015; Williams et al., 2018) largely because it facilitates untrained annotators' participation in dataset creation.
NLI itself has been steadily gaining in popularity; many datasets for training and/or testing systems are now available including: FraCaS (Cooper et al., 1994), RTE (Dagan et al., 2006; Mirkin et al., 2009; Dagan et al., 2013), Sentences Involving Compositional Knowledge (Marelli et al., 2014, SICK), large scale imaging captioning as NLI (Bowman et al., 2015, SNLI), recasting other datasets into NLI (Glickman, 2006; White et al., 2017; Poliak et al., 2018), ordinal commonsense inference (Zhang et al., 2017, JOCI), Multi-Premise Entailment (Lai et al., 2017, MPE), NLI over multiple genres of written and spoken English (Williams et al., 2018, MultiNLI), adversarially filtered common sense reasoning sentences (Zellers et al., 2018, 2019, (Hella)SWAG), explainable annotations for SNLI (Camburu et al., 2018, e-SNLI), cross-lingual NLI (Conneau et al., 2018, XNLI), scientific questioning answering as NLI (Khot et al., 2018, SciTail), NLI recast-question answering (part of Wang et al. 2019, GLUE), NLI for dialog (Welleck et al., 2019), and NLI over narratives that require drawing inferences to the most plausible explanation from text (Bhagavatula et al., 2020,  $\alpha$ NLI). Other NLI datasets are created to identify where models fail (Glockner et al., 2018; Naik et al., 2018; McCoy et al., 2019; Schmitt and Schütze, 2019), many of which are also automatically generated (Geiger et al., 2018; Yanaka et al., 2019a,b; Kim et al., 2019; Nie et al., 2019; Richardson et al., 2020).
As datasets for NLI become increasingly numerous, one might wonder, do we need yet another NLI dataset? In this case, the answer is clearly yes: despite NLI's formulation as a commonsense reasoning task, it is still unknown whether this framing has resulted in models that learn specific modes of pragmatic reasoning. IMPPRES is the first NLI dataset to explicitly probe whether models trained on commonsense reasoning actually do treat pragmatic inferences like implicatures and presuppositions as entailments without additional training on these specific inference types.
Beyond NLI, several recent works introduce resources for evaluating sentence understanding models for knowledge of pragmatic inferences. On the presupposition side, datasets such as MegaVeridicality (White and Rawlins, 2018) and CommitmentBank (de Marneffe et al., 2019) compile gradient crowdsourced judgments regarding how likely a clause embedding predicate is to trig
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ger a presupposition that its complement clause is true. White et al. (2018) and Jiang and de Marn-effe (2019) find that LSTMs trained on a gradient event factuality prediction task on these respective datasets make systematic errors. Turning to implicatures, Degen (2015) introduces a dataset measuring the strength of the implicature from some to not all with crowd-sourced judgments. Schuster et al. (2020) find that an LSTM with supervision on this dataset can predict human judgments well. These resources all differ from IMPPRES in two respects: First, their empirical scopes are all somewhat narrower, as all these datasets focus on only a single class of presupposition or implicature triggers. Second, the use of gradient judgments makes it non-trivial to use these datasets to evaluate NLI models, which are trained to make categorical predictions about entailment. Both approaches have advantages, and we leave a direct comparison for future work.
Outside the topic of sentential inference, Rashkin et al. (2018) propose a new task where a model must label actor intents and reactions for particular actions described using text. Cianflone et al. (2018) create sentence-level adverbial presupposition datasets and train a binary classifier to detect contexts in which presupposition triggers (e.g., too, again) can be used.
4 Annotating MultiNLI for Pragmatics
In this section, we present results of an annotation effort that show that MultiNLI contains very little explicit evidence of pragmatic inferences of the type tested by IMPPRES. Although Williams et al. (2018) report that  $22\%$  of the MultiNLI development set sentence pairs contain lexical triggers (such as regret or stopped) in the premise and/or hypothesis, the mere presence of presupposition-triggering lexical items in the data does not show that MultiNLI contains evidence that presuppositions are entailments, since the sentential inference may focus on other types of information. To address this, we randomly selected 200 sentence pairs from the MultiNLI matched development set and presented them to three expert annotators with a combined total of 17 years of training in formal semantics and pragmatics.4 Annotators answered the following questions for each pair: (1) are the sentences  $P$  and  $H$  related by a presupposition/implicature relation (entails/is en
tailed by, negated or not); (2) what subtype of inference (e.g., existence presupposition,  $\langle$  some, all  $\rangle$  implicature); (3) is the presupposition trigger embedded under an entailment-cancelling operator?
Agreement among annotators was low, suggesting that few MultiNLI pairs are paradigmatic cases of implicatures or presuppositions. We found only 8 presupposition pairs and 3 implicature pairs on which two or more annotators agreed. Moreover, we found only one example illustrating a particular inference type tested in IMPPRES (the presupposition of possessed definites). All others were tagged as existence presuppositions and conversational implicatures (i.e. loose inferences dependent on world knowledge). The union of annotations was much larger:  $42\%$  of examples were identified by at least one annotator as a presupposition or implicature (51 presuppositions and 42 implicatures, with 10 sentences receiving divergent tags). However, of these, only 23 presuppositions and 19 implicatures could reliably be used to learn pragmatic inference (in 14 cases, the given tag did not match the pragmatic inference, and in 27 cases, computing the inference did not affect the relation type). Again, the large majority of implicatures were conversational, and most presuppositions were existential, and generally not linked to particular lexical triggers (e.g., topic marking).
We conclude that the MultiNLI dataset at best contains some evidence of loose pragmatic reasoning based on world knowledge and discourse structure, but almost no explicit information relevant to lexically triggered pragmatic inference, which is of the type tested in this paper.
Data Generation. IMPPRES consists of semiautomatically generated pairs of sentences with NLI labels illustrating key properties of implicatures and presuppositions. We generate IMPPRES using a codebase developed by Warstadt et al. (2019a) and significantly expanded for the BLiMP dataset (Warstadt et al., 2019b). The codebase, including our scripts and documentation, are publicly available. Each sentence type in IMPPRES is generated according to a template that specifies the linear order of the constituents in the sentence. The constituents are sampled from a vocabulary of over 3000 lexical items annotated with grammatical features needed to ensure morphological,
4The full annotations are on the IMPPRES repository.
5 github.com/alexwarstadt/data_generation
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Premise	Hypothesis	Relation type	Logical label	Pragmatic label	Item type
some	not all	implicature (+ to -)	neutral	entailment	target
not all	some	implicature (- to +)	neutral	entailment	target
some	all	negated implicature (+)	neutral	contradiction	target
all	some	reverse negated implicature (+)	entailment	contradiction	target
not all	none	negated implicature (-)	neutral	contradiction	target
none	not all	reverse negated implicature (-)	entailment	contradiction	target
all	none	opposite	contradiction	contradiction	control
none	all	opposite	contradiction	contradiction	control
some	none	negation	contradiction	contradiction	control
none	some	negation	contradiction	contradiction	control
all	not all	negation	contradiction	contradiction	control
not all	all	negation	contradiction	contradiction	control
Table 2: Paradigm for the scalar implicature datasets, with  $\langle \text{some}, \text{all} \rangle$  as an example.
syntactic, and semantic well-formedness. All sentences generated from a given template are structurally analogous up to the specified constituents, but may vary in sub-constituents. For instance, if the template calls for a verb phrase, the generated constituent may include a direct object or complement clause, depending on the argument structure of the sampled verb. See §6.1 and 7.1 for descriptions of the sentence types in the implicature and presupposition data.
Generating data lets us control the lexical and syntactic content so that we can guarantee that the sentence pairs in IMPPRES evaluate the desired phenomenon (see Ettinger et al., 2016, for related discussion). Furthermore, the codebase we use allows for greater lexical and syntactic variety than in many other templatic datasets (see discussion in Warstadt et al., 2019b). One limitation of this methodology is that generated sentences, while generally grammatical, often describe highly unlikely scenarios, or include low frequency combinations of lexical items (e.g., Sabrina only reveals this pasta). Another limitation is that generated data is of limited use for training models, since it contains simple regularities that supervised classifiers may learn to exploit. Thus, we create IMPPRES solely for the purpose of evaluating NLI models trained on standard datasets like MultiNLI.
Models. Our experiments evaluate NLI models trained on MultiNLI and built on top of three sentence encoding models: a bag of words (BOW) model, InferSent (Conneau et al., 2017), and BERT-Large (Devlin et al., 2019). The BOW and InferSent models use 300D GloVe embeddings as word representations (Pennington et al., 2014). For the BOW baseline, word embeddings for premise and hypothesis are separately summed
to create sentence representations, which are concatenated to form a single sentence-pair representation which is fed to a logistic regression softmax classifier. For the InferSent model, GloVe embeddings for the words in premise and hypothesis are respectively fed into a bidirectional LSTM, after which we concatenate the representations for premise and hypothesis, their difference, and their element-wise product (Mou et al., 2016). BERT is a multilayer bidirectional transformer pretrained with the masked language modelling and next sequence prediction objectives, and finetuned on the MultiNLI dataset. We concatenate the premise and hypothesis after a special [CLS] token and separated them with the [SEP] token. The BERT representation for the [CLS] token is fed into classifier. We use Huggingface's pre-trained BERT trained on Toronto books (Zhu et al., 2015).<sup>6</sup>
The BOW and InferSent models have development set accuracies of  $49.6\%$  and  $67.6\%$ . The development set accuracy for BERT-Large on MultiNLI is  $86.6\%$ , similar to the results achieved by (Devlin et al., 2019), but somewhat lower than state-of-the-art (currently  $90.8\%$  on test from the ensembled RoBERTa model with long pretraining optimization, Liu et al. 2019).
6 Experiment 1: Scalar Implicatures
6.1 Scalar Implicature Datasets
The scalar implicature portion of IMPPRES includes six datasets, each isolating a different scalar implicature trigger from six types of lexical scales (of the type described in §2): determiners ⟨some, all⟩, connectives ⟨or, and⟩, modals ⟨can, have to⟩, numerals ⟨2,3⟩, ⟨10,100⟩, scalar adjectives, and
$^{6}$ github.com/huggingface/pytorch-pretrained-BERT/
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Figure 2: Results on Controls (Implicatures)
Figure 3: Results on Target Conditions (Implicatures)
verbs, e.g., <good, excellent>, <run, sprint>. Examples pairs of each implicature trigger can be found in Table 4 in the Appendix. For each type, we generate 100 paradigms, each consisting of 12 unique sentence pairs, as shown in Table 2.
The six target sentence pairs comprise two main relation types: 'implicature' and 'negated implicature'. Pairs tagged as 'implicature' have a premise that implicates the hypothesis (e.g., some and not all). For 'negated implicature', the premise implicates the negation of the hypothesis (e.g., some and all), or vice versa (e.g., all and some). Six control pairs are logical contradictions, representing either scalar 'opposites' (e.g., all and none), or 'negations' (e.g., not all and all; some and none), probing the models' basic grasp of negation.
As mentioned in §2.1, implicature computation is variable and dependent on the context of utterance. Thus, we anticipate two possible rational behaviors for a MultiNLI-trained model tested on an implicature: (a) be pragmatic, and compute the implicature, concluding that the premise and hypothesis are in an 'entailment' relation, (b) be logical, i.e., consider only the literal content, and not compute the implicature, concluding they are in a 'neutral' relation. Thus, we measure both possible conclusions, by tagging sentence pairs for scalar implicature with two sets of NLI labels to reflect the behavior expected under "logical" and "pragmatic" modes of inference, as shown in Table 2.
6.2 Implicatures Results & Discussion
We first evaluate model performance on the controls, shown in Figure 2. Success on these controls is a necessary condition for us to conclude that a model has learned the basic function of negation (not, none, neither) and the scalar relationship between terms like some and all. We find that BERT performs at ceiling on control conditions for all implicature types, in contrast with InferSent and BOW, whose performance is very variable. Since only BERT passes all controls, its results on the target items are most interpretable. Full results for all models and target conditions by implicature trigger are in Figures 8-13 in the Appendix.