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Found 21 papers, 7 papers with code
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability.
When classifying grammatical role, BERT doesn’t care about word order... except when it matters
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
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
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.
A Discerning Several Thousand Judgments: GPT-3 Rates the Article + Adjective + Numeral + Noun Construction
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
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
This allows us to encode robust, position-independent character-level information in the internal representations of subword-based models.
Why is Winoground Hard? Investigating Failures in Visuolinguistic Compositionality
Recent visuolinguistic pre-trained models show promising progress on various end tasks such as image retrieval and video captioning.
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability.
What do tokens know about their characters and how do they know it?
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
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
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
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?
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
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.
Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERT
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
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.
Response to Liu, Xu, and Liang (2015) and Ferrer-i-Cancho and Gómez-Rodríguez (2015) on Dependency Length Minimization
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).
