Syntax and Semantics Meet in the "Middle": Probing the Syntax-Semantics Interface of LMs Through Agentivity

• URL: http://arxiv.org/abs/2305.18185v2
• Date: Mon, 10 Jul 2023 13:10:40 GMT
• Title: Syntax and Semantics Meet in the "Middle": Probing the Syntax-Semantics Interface of LMs Through Agentivity
• Authors: Lindia Tjuatja, Emmy Liu, Lori Levin, Graham Neubig
• Abstract summary: We present the semantic notion of agentivity as a case study for probing such interactions. This suggests LMs may potentially serve as more useful tools for linguistic annotation, theory testing, and discovery.
• Score: 68.8204255655161
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recent advances in large language models have prompted researchers to examine their abilities across a variety of linguistic tasks, but little has been done to investigate how models handle the interactions in meaning across words and larger syntactic forms -- i.e. phenomena at the intersection of syntax and semantics. We present the semantic notion of agentivity as a case study for probing such interactions. We created a novel evaluation dataset by utilitizing the unique linguistic properties of a subset of optionally transitive English verbs. This dataset was used to prompt varying sizes of three model classes to see if they are sensitive to agentivity at the lexical level, and if they can appropriately employ these word-level priors given a specific syntactic context. Overall, GPT-3 text-davinci-003 performs extremely well across all experiments, outperforming all other models tested by far. In fact, the results are even better correlated with human judgements than both syntactic and semantic corpus statistics. This suggests that LMs may potentially serve as more useful tools for linguistic annotation, theory testing, and discovery than select corpora for certain tasks. Code is available at https://github.com/lindiatjuatja/lm_sem

Related papers

• Revisiting a Pain in the Neck: Semantic Phrase Processing Benchmark for Language Models [10.482557806309174]
  We introduce LexBench, a comprehensive evaluation suite enabled to test language models (LMs) on semantic phrase processing tasks. Thanks to ourbenchmark, we assess the performance of 15 LMs across model architectures and parameter scales in classification, extraction, and interpretation tasks. Our benchmarking findings can serve future research aiming to improve the generic capability of LMs on semantic phrase comprehension.
  arXiv  Detail & Related papers  (2024-05-05T09:20:38Z)
• Agentivit\`a e telicit\`a in GilBERTo: implicazioni cognitive [77.71680953280436]
  The goal of this study is to investigate whether a Transformer-based neural language model infers lexical semantics. The semantic properties considered are telicity (also combined with definiteness) and agentivity.
  arXiv  Detail & Related papers  (2023-07-06T10:52:22Z)
• Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models [9.808214545408541]
  LinguisticLens is a novel inter-active visualization tool for making sense of and analyzing syntactic diversity of datasets. It supports hierarchical visualization of a text dataset, allowing users to quickly scan for an overview and inspect individual examples.
  arXiv  Detail & Related papers  (2023-05-19T00:53:45Z)
• Evaluating statistical language models as pragmatic reasoners [39.72348730045737]
  We evaluate the capacity of large language models to infer meanings of pragmatic utterances. We find that LLMs can derive context-grounded, human-like distributions over the interpretations of several complex pragmatic utterances. Results inform the inferential capacity of statistical language models, and their use in pragmatic and semantic parsing applications.
  arXiv  Detail & Related papers  (2023-05-01T18:22:10Z)
• TERMinator: A system for scientific texts processing [0.0]
  This paper is devoted to the extraction of entities and semantic relations between them from scientific texts. We present a dataset that includes annotations for two tasks and develop a system called TERMinator for the study of the influence of language models on term recognition.
  arXiv  Detail & Related papers  (2022-09-29T15:14:42Z)
• Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution [124.99894592871385]
  We present a large-scale comparative study of lexical substitution methods employing both old and most recent language models. We show that already competitive results achieved by SOTA LMs/MLMs can be further substantially improved if information about the target word is injected properly.
  arXiv  Detail & Related papers  (2022-06-07T16:16:19Z)
• Infusing Finetuning with Semantic Dependencies [62.37697048781823]
  We show that, unlike syntax, semantics is not brought to the surface by today's pretrained models. We then use convolutional graph encoders to explicitly incorporate semantic parses into task-specific finetuning.
  arXiv  Detail & Related papers  (2020-12-10T01:27:24Z)
• Words aren't enough, their order matters: On the Robustness of Grounding Visual Referring Expressions [87.33156149634392]
  We critically examine RefCOg, a standard benchmark for visual referring expression recognition. We show that 83.7% of test instances do not require reasoning on linguistic structure. We propose two methods, one based on contrastive learning and the other based on multi-task learning, to increase the robustness of ViLBERT.
  arXiv  Detail & Related papers  (2020-05-04T17:09:15Z)
• Information-Theoretic Probing for Linguistic Structure [74.04862204427944]
  We propose an information-theoretic operationalization of probing as estimating mutual information. We evaluate on a set of ten typologically diverse languages often underrepresented in NLP research.
  arXiv  Detail & Related papers  (2020-04-07T01:06:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.