Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference (P19-1)
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| Challenge: | lexical overlap heuristics are effective for frequent example types but break down in more challenging cases. |
| Approach: | They propose to use a set of examples to test whether a sentence entails another . they propose to adopt three fallible syntactic heuristics for statistical NLI models . |
| Outcome: | The proposed model performs poorly on the HANS dataset, suggesting they have adopted these heuristics. |
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Breaking NLI Systems with Sentences that Require Simple Lexical Inferences (P18-2)
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| Challenge: | a new test set shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge. |
| Approach: | They create a new NLI test set that shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge. |
| Outcome: | The new examples are simpler than the SNLI test set, but the state-of-the-art systems perform poorly on it. |
Simple but Challenging: Natural Language Inference Models Fail on Simple Sentences (2022.findings-emnlp)
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| Challenge: | Natural language inference (NLI) tasks are difficult to perform on large datasets . a small number of simple sentences can improve model performance, authors say . |
| Approach: | They propose to use syntactically simple sentences to test the inference ability of NLI models. |
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Evaluating BERT for natural language inference: A case study on the CommitmentBank (D19-1)
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| Challenge: | Natural language inference datasets can identify premise-hypothesis relationship without observing premise . recasting of the CommitmentBank for NLI creates hypotheses that stand in entailment/contradiction/neutral relationship with premise. |
| Approach: | They propose to recast the CommitmentBank for NLI to stand in certain relationships with the premise . hypotheses are complements of clause-embedding verbs in each premise, rethinking the CommittedBank . |
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Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition (2020.acl-main)
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| Challenge: | Natural language inference (NLI) is an increasingly important task for natural language understanding . however, the ability of NLI models to make pragmatic inferences remains understudied . |
| Approach: | They use semi-automatically generated sentence pairs to evaluate whether NLI models make pragmatic inferences. |
| Outcome: | The proposed model trains on multiNLI and shows that it learns to draw pragmatic inferences. |
He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics (2021.tacl-1)
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| Challenge: | Existing models for factuality prediction are lacking for English . Traditionally, event factualism is triggered by fixed properties of lexical items . |
| Approach: | They propose a model that exploits common surface patterns that correlate with factuality labels. |
| Outcome: | The proposed model achieves the best performance on four factuality datasets. |
Syntactic Data Augmentation Increases Robustness to Inference Heuristics (2020.acl-main)
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| Challenge: | Pretrained neural models lack sensitivity to word order on controlled challenge sets . augmentation methods that improve accuracy on standard training sets may be a problem . |
| Approach: | They propose to augment standard training sets with syntactically informative examples by applying syntastic transformations to sentences from the MNLI corpus. |
| Outcome: | The proposed method improved BERT’s accuracy on controlled examples that diagnose sensitivity to word order from 0.28 to 0.73 without affecting performance on the MNLI test set. |
IMPLI: Investigating NLI Models’ Performance on Figurative Language (2022.acl-long)
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| Challenge: | Understanding figurative language is a difficult area in NLP but is essential for proper understanding. |
| Approach: | They propose to use a dataset to generate 24k semiautomatic pairs and manually create 1.8k gold pairs to evaluate NLI models. |
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What BERT Is Not: Lessons from a New Suite of Psycholinguistic Diagnostics for Language Models (2020.tacl-1)
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| Challenge: | Pretraining by language modeling has become popular but we have yet to understand what language models learn during that process. |
| Approach: | They propose diagnostics that ask questions about information used by language models for generating predictions in context. |
| Outcome: | The proposed diagnostics can be used to study the popular BERT model . they show that the model can distinguish good from bad completions, but struggles with inference and role-based event prediction. |
Deep Learning for Natural Language Inference (N19-5)
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| Challenge: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning for language understanding and reasoning. |
| Approach: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development and cutting- edge deep learning models. |
| Outcome: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning model for language understanding and reasoning. |
Looking at the Overlooked: An Analysis on the Word-Overlap Bias in Natural Language Inference (2022.emnlp-main)
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| Challenge: | Existing methods for debiasing are ineffective in addressing the reverse word-overlap bias. |
| Approach: | They propose to investigate the reverse word-overlap bias in NLI models . they find that existing debiasing methods are generally ineffective . |
| Outcome: | The proposed model is biased towards the non-entailment label on instances with low overlap . the proposed model does not have minority examples, the authors show . |