More Like this

1. Toward an accommodation account of deaccenting under nonidentity

   https://doi.org/10.3765/elm.1.4875  

   Geiger, Jeffrey; Xiang, Ming (July 2021, Experiments in Linguistic Meaning)

   Beltrama, Andrea; Schwarz, Florian; Papagragou, Anna (Ed.)

   Two competing models attempt to explain the deaccentuation of antecedent nonidentical discourse-inferable material (e.g., Bach wrote many pieces for viola. He must have LOVED string instruments). One uses a single grammatical constraint to license deaccenting for identical and nonidentical material. The second licenses deaccenting grammatically only for identical constituents, whereas deaccented nonidentical material requires accommodation of an alternative antecedent. In three experiments, we tested listeners’ preferences for accentuation or deaccentuation on nonidentical inferable material in out-of-the-blue contexts, supportive discourse contexts, and in the presence of the presupposition trigger too. The results indicate that listeners by default prefer for inferable material to be accented, but that this preference can be mitigated or even reversed with the help of manipulations in the broader discourse context. By contrast, listeners reliably preferred for repeated material to be deaccented. We argue that these results are more compatible with the accommodation model of deaccenting licensing, which allows for differential licensing of deaccentuation on inferable versus repeated constituents and provides a principled account of the sensitivity of accentuation preferences on inferable material to broader contextual manipulations. 

   more » « less
2. Discourse‐level adaptation in pronoun comprehension

   https://doi.org/10.1111/lnc3.12481  

   Ye, Yining; Arnold, Jennifer E. (January 2023, Language and Linguistics Compass)

   Abstract It is well established that people adapt to statistical regularities at phonological, lexical, and syntactic levels. Much less is known about adaptation to discourse‐level structures, such as adaptation to structures defined as the relationship between a pronoun and its antecedent. To fill this gap, this paper reviews studies on the learning of referential patterns by asking (1) do people represent referential structures, (2) how long do discourse‐level representations last, (3) how specific are representations that are used for referential adaptation, (4) what mechanisms underlie this adaptation, and (5) what the current methods are used to test referential adaptation. This paper also briefly summarises the work on adaptation at other linguistic levels. This line of work extends adaptation to higher‐level structures and demonstrates how people learn language patterns that drive successful communication and reading skills. 

   more » « less
3. Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion

   Ko, Wei-Jen; Wu, Yating; Dalton, Cutter; Srinivas, Dananjay; Durrett, Greg; Li, Junyi Jessy (July 2023, Findings of the Association for Computational Linguistics: ACL 2023)

   Automatic discourse processing is bottlenecked by data: current discourse formalisms pose highly demanding annotation tasks involving large taxonomies of discourse relations, making them inaccessible to lay annotators. This work instead adopts the linguistic framework of Questions Under Discussion (QUD) for discourse analysis and seeks to derive QUD structures automatically. QUD views each sentence as an answer to a question triggered in prior context; thus, we characterize relationships between sentences as free-form questions, in contrast to exhaustive fine-grained taxonomies. We develop the first-of-its-kind QUD parser that derives a dependency structure of questions over full documents, trained using a large, crowdsourced question-answering dataset DCQA (Ko et al., 2022). Human evaluation results show that QUD dependency parsing is possible for language models trained with this crowdsourced, generalizable annotation scheme. We illustrate how our QUD structure is distinct from RST trees, and demonstrate the utility of QUD analysis in the context of document simplification. Our findings show that QUD parsing is an appealing alternative for automatic discourse processing. 

   more » « less
4. The Role of Pragmatic and Discourse Context in Determining Argument Impact

   https://doi.org/10.18653/v1/d19-1568  

   Durmus, Esin; Ladhak, Faisal; Cardie, Claire (November 2019, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP))

   null (Ed.)

   Research in the social sciences and psychology has shown that the persuasiveness of an argument depends not only the language employed, but also on attributes of the source/communicator, the audience, and the appropriateness and strength of the argument’s claims given the pragmatic and discourse context of the argument. Among these characteristics of persuasive arguments, prior work in NLP does not explicitly investigate the effect of the pragmatic and discourse context when determining argument quality. This paper presents a new dataset to initiate the study of this aspect of argumentation: it consists of a diverse collection of arguments covering 741 controversial topics and comprising over 47,000 claims. We further propose predictive models that incorporate the pragmatic and discourse context of argumentative claims and show that they outperform models that rely only on claim-specific linguistic features for predicting the perceived impact of individual claims within a particular line of argument. 

   more » « less
5. Understanding Collaborative Learning Processes and Outcomes Through Student Discourse Dynamics

   https://doi.org/10.1145/3706468.3706547  

   Park, Seehee; Nixon, Nia; D'Mello, Sidney; Shariff, Danielle; Choi, Jaeyoon (March 2025, ACM)

   This study explores the relation between students’ discourse dynamics and performance during collaborative problem-solving activities utilizing Linguistic Inquiry Word Count (LIWC). We analyzed linguistic variables from students’ communications to explore social and cognitive behavior. Participants include 279 undergraduate students from two U.S. universities engaged in a controlled lab setting using the physics related educational game named Physics Playground. Findings highlight the relationship between social and cognitive linguistic variables and student’s physics performance outcome in a virtual collaborative learning context. This study contributes to a deeper understanding of how these discourse dynamics are related to learning outcomes in collaborative learning. It provides insights for optimizing educational strategies in collaborative remote learning environments. We further discuss the potential for conducting computational linguistic modeling on learner discourse and the role of natural language processing in deriving insights on learning behavior to support collaborative learning. 

   more » « less