Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs

Warstadt, Alex; Cao, Yu; Grosu, Ioana Georgeta; Wei, Ping; Blix, Hagen; Nie, Yining; Alsop, Anna; Bordia, Shikha; Liu, Haokun; Parrish, Alicia; Wang, Shengfu; Phang, Jason; Mohananey, Anhad; Htut, Phu Mon; Jeretič, Paloma; Bowman, Samuel R.

doi:10.48550/arxiv.1909.02597

Cited by 5 publications

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“…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). We generate IMPPRES according to expert-crafted grammars using a codebase developed by Warstadt et al (2019). The codebase includes a vocabulary of over 3000 lexical items annotated with grammatical features needed to ensure morphological, syntactic, and semantic well-formedness.…”

Section: Methodsmentioning

confidence: 99%

Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition

Jeretič¹,

Warstadt²,

Bhooshan³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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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.

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Section: Methodsmentioning

confidence: 99%

Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition

Jeretič¹,

Warstadt²,

Bhooshan³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Self Cite

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“…This was the case even for sentences with distractor clauses between the subject and the verb, and meaningless sentences. A study of negative polarity items (NPIs) by Warstadt et al (2019) showed that BERT is better able to detect the presence of NPIs (e.g. "ever") and the words that allow their use (e.g.…”

Section: Linmentioning

confidence: 99%

“…Furthermore, different probing methods may reveal complementary or even contradictory information, in which case a single test (as done in most studies) would not be sufficient (Warstadt et al, 2019). Certain methods might also favor a certain model, e.g., RoBERTa is trailing BERT with one tree extraction method, but leading with another (Htut et al, 2019).…”

Section: Limitationsmentioning

confidence: 99%

A Primer in BERTology: What We Know About How BERT Works

Rogers

Kovaleva

Rumshisky

2020

Transactions of the Association for Computational Linguistics

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Transformer-based models have pushed state of the art in many areas of NLP, but our understanding of what is behind their success is still limited. This paper is the first survey of over 150 studies of the popular BERT model. We review the current state of knowledge about how BERT works, what kind of information it learns and how it is represented, common modifications to its training objectives and architecture, the overparameterization issue, and approaches to compression. We then outline directions for future research.

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“…However, we also recognize that probing is one tool in the evaluation toolkit, and its results must be interpreted in context( [14], [15], [16]). To complement probing, future work might investigate model competence using tasks that require integrating multiple types of linguistic knowledge.…”

Section: Investigation Of Linguistic Information Through Probingmentioning

confidence: 99%

Decoding the Encoded – Linguistic Secrets of Language Models: A Systematic Literature Review

Avetisyan,

Broneske

2023

Machine Learning Techniques and NLP

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Language models’ growing role in natural language processing neces- sitates a deeper understanding of their linguistic knowledge. Linguistic probing tasks have become crucial for model explainability, designed to evaluate models’ understanding of vari-ous linguistic phenomena. Objective: This systematic review critically assesses the linguistic knowledge of language models via linguistic probing, providing a comprehensive overview ofthe understood linguistic phenomena and identifying future research areas. Method: We performed an extensive search of relevant academic databases and analyzed 57 articles pub- lished between October 2018 and October 2022. Results: While language models exhibit extensive linguistic knowledge, limitations persist in their comprehension of specific phe- nomena. The review also points to a need for consensus on evaluating language models’ linguistic knowledge and the linguistic terminology used. Conclusion: Our review offers an extensive look into linguistic knowledge of language models through linguistic probing tasks. This study underscores the importance of understanding these models’ linguistic capabilities for effective use in NLP applications and for fostering more explainable AI systems.

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Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs

Cited by 5 publications

References 0 publications

Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition

Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition

A Primer in BERTology: What We Know About How BERT Works

Decoding the Encoded – Linguistic Secrets of Language Models: A Systematic Literature Review

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