Search Results for author: Kyle Mahowald

Found 21 papers, 7 papers with code

When classifying grammatical role, BERT doesn’t care about word order... except when it matters

no code implementations ACL 2022 Isabel Papadimitriou, Richard Futrell, Kyle Mahowald

Because meaning can often be inferred from lexical semantics alone, word order is often a redundant cue in natural language.

Investigating Information-Theoretic Properties of the Typology of Spatial Demonstratives

no code implementations NAACL (SIGTYP) 2022 Sihan Chen, Richard Futrell, Kyle Mahowald

Using data from Nintemann et al. (2020), we explore the variability in complexity and informativity across spatial demonstrative systems using spatial deictic lexicons from 223 languages.

Keep it Neutral: Using Natural Language Inference to Improve Generation

no code implementations16 Feb 2023 Michail Mersinias, Kyle Mahowald

We explore incorporating natural language inference (NLI) into the text generative pipeline by using a pre-trained NLI model to assess whether a generated sentence entails, contradicts, or is neutral to the prompt and preceding text.

Natural Language Inference Text Generation

A Discerning Several Thousand Judgments: GPT-3 Rates the Article + Adjective + Numeral + Noun Construction

no code implementations29 Jan 2023 Kyle Mahowald

I validate the prompt using the CoLA corpus of acceptability judgments and then zero in on the AANN construction.


Dissociating language and thought in large language models: a cognitive perspective

no code implementations16 Jan 2023 Kyle Mahowald, Anna A. Ivanova, Idan A. Blank, Nancy Kanwisher, Joshua B. Tenenbaum, Evelina Fedorenko

Here, we review the capabilities of LLMs by considering their performance on two different aspects of language use: 'formal linguistic competence', which includes knowledge of rules and patterns of a given language, and 'functional linguistic competence', a host of cognitive abilities required for language understanding and use in the real world.

Inducing Character-level Structure in Subword-based Language Models with Type-level Interchange Intervention Training

no code implementations19 Dec 2022 Jing Huang, Zhengxuan Wu, Kyle Mahowald, Christopher Potts

This allows us to encode robust, position-independent character-level information in the internal representations of subword-based models.

Spelling Correction

Why is Winoground Hard? Investigating Failures in Visuolinguistic Compositionality

1 code implementation1 Nov 2022 Anuj Diwan, Layne Berry, Eunsol Choi, David Harwath, Kyle Mahowald

Recent visuolinguistic pre-trained models show promising progress on various end tasks such as image retrieval and video captioning.

Data Augmentation Image Retrieval +2

What do tokens know about their characters and how do they know it?

1 code implementation NAACL 2022 Ayush Kaushal, Kyle Mahowald

Pre-trained language models (PLMs) that use subword tokenization schemes can succeed at a variety of language tasks that require character-level information, despite lacking explicit access to the character composition of tokens.

When classifying grammatical role, BERT doesn't care about word order... except when it matters

1 code implementation11 Mar 2022 Isabel Papadimitriou, Richard Futrell, Kyle Mahowald

Because meaning can often be inferred from lexical semantics alone, word order is often a redundant cue in natural language.

Experimentally measuring the redundancy of grammatical cues in transitive clauses

no code implementations30 Jan 2022 Kyle Mahowald, Evgeniia Diachek, Edward Gibson, Evelina Fedorenko, Richard Futrell

The conclusion is that grammatical cues such as word order are necessary to convey agenthood and patienthood in only at most 10-15% of naturally occurring sentences; nevertheless, they can (a) provide an important source of redundancy and (b) are crucial for conveying intended meaning that cannot be inferred from the words alone, including descriptions of human interactions, where roles are often reversible (e. g., Ray helped Lu/Lu helped Ray), and expressing non-prototypical meanings (e. g., "The bone chewed the dog.

A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space

1 code implementation EMNLP 2021 Alex Jones, William Yang Wang, Kyle Mahowald

We verify some of our linguistic findings by looking at the effect of morphological segmentation on English-Inuktitut alignment, in addition to examining the effect of word order agreement on isomorphism for 66 zero-shot language pairs from a different corpus.


How (Non-)Optimal is the Lexicon?

no code implementations NAACL 2021 Tiago Pimentel, Irene Nikkarinen, Kyle Mahowald, Ryan Cotterell, Damián Blasi

Examining corpora from 7 typologically diverse languages, we use those upper bounds to quantify the lexicon's optimality and to explore the relative costs of major constraints on natural codes.

Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP

1 code implementation NeurIPS 2021 Joshua Rozner, Christopher Potts, Kyle Mahowald

Cryptic crosswords, the dominant crossword variety in the UK, are a promising target for advancing NLP systems that seek to process semantically complex, highly compositional language.

Language Modelling

Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERT

1 code implementation EACL 2021 Isabel Papadimitriou, Ethan A. Chi, Richard Futrell, Kyle Mahowald

Further examining the characteristics that our classifiers rely on, we find that features such as passive voice, animacy and case strongly correlate with classification decisions, suggesting that mBERT does not encode subjecthood purely syntactically, but that subjecthood embedding is continuous and dependent on semantic and discourse factors, as is proposed in much of the functional linguistics literature.

With Little Power Comes Great Responsibility

1 code implementation EMNLP 2020 Dallas Card, Peter Henderson, Urvashi Khandelwal, Robin Jia, Kyle Mahowald, Dan Jurafsky

Despite its importance to experimental design, statistical power (the probability that, given a real effect, an experiment will reject the null hypothesis) has largely been ignored by the NLP community.

Experimental Design Machine Translation +1

Response to Liu, Xu, and Liang (2015) and Ferrer-i-Cancho and Gómez-Rodríguez (2015) on Dependency Length Minimization

no code implementations1 Oct 2015 Richard Futrell, Kyle Mahowald, Edward Gibson

We address recent criticisms (Liu et al., 2015; Ferrer-i-Cancho and G\'omez-Rodr\'iguez, 2015) of our work on empirical evidence of dependency length minimization across languages (Futrell et al., 2015).

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