{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/145598"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/145598","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Cause, Composition, and Structure in Language","abstract":"From everyday communication to exploring new thoughts through writing, humans use language in a remarkably flexible, robust, and creative way. In this thesis, I present three case studies supporting the overarching hypothesis that linguistic knowledge in the human mind can be understood as hierarchically-structured causal generative models, within which a repertoire of compositional inference motifs support efficient inference. I begin with a targeted case study showing how native speakers follow principles of noisy-channel inference in resolving subject-verb agreement mismatches such as \"The gift for the kids are hidden under the bed\". Results suggest that native-speakers' inferences reflect both prior expectations and structure-sensitive conditioning of error probabilities consistent with the statistics of the language production environment. Second, I develop a more open-ended inferential challenge, completing fragmentary linguistic inputs such as \"____ published won ____.\" into well-formed sentences. I use large-scale neural language models to compare two classes of models on this task: the task-specific fine-tuning approach standard in AI and NLP, versus an inferential approach involving composition of two simple computational motifs; the inferential approach yields more human-like completions. Third, I show that incorporating hierarchical linguistic structure into one of these computational motifs, namely the auto-regressive word prediction task, yields improvements in neural language model performance on targeted evaluations of models’ grammatical capabilities. I conclude by suggesting future directions in understanding the form and content of these causal generative models of human language.","abstract_html":"From everyday communication to exploring new thoughts through writing, humans use language in a remarkably flexible, robust, and creative way. In this thesis, I present three case studies supporting the overarching hypothesis that linguistic knowledge in the human mind can be understood as hierarchically-structured causal generative models, within which a repertoire of compositional inference motifs support efficient inference. I begin with a targeted case study showing how native speakers follow principles of noisy-channel inference in resolving subject-verb agreement mismatches such as "The gift for the kids are hidden under the bed". Results suggest that native-speakers' inferences reflect both prior expectations and structure-sensitive conditioning of error probabilities consistent with the statistics of the language production environment. Second, I develop a more open-ended inferential challenge, completing fragmentary linguistic inputs such as "____ published won ____." into well-formed sentences. I use large-scale neural language models to compare two classes of models on this task: the task-specific fine-tuning approach standard in AI and NLP, versus an inferential approach involving composition of two simple computational motifs; the inferential approach yields more human-like completions. Third, I show that incorporating hierarchical linguistic structure into one of these computational motifs, namely the auto-regressive word prediction task, yields improvements in neural language model performance on targeted evaluations of models’ grammatical capabilities. I conclude by suggesting future directions in understanding the form and content of these causal generative models of human language.","abstract_has_math":false,"creators":["Qian, Peng"],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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In this thesis, I present three case studies supporting the overarching hypothesis that linguistic knowledge in the human mind can be understood as hierarchically-structured causal generative models, within which a repertoire of compositional inference motifs support efficient inference. I begin with a targeted case study showing how native speakers follow principles of noisy-channel inference in resolving subject-verb agreement mismatches such as \"The gift for the kids are hidden under the bed\". Results suggest that native-speakers' inferences reflect both prior expectations and structure-sensitive conditioning of error probabilities consistent with the statistics of the language production environment. Second, I develop a more open-ended inferential challenge, completing fragmentary linguistic inputs such as \"____ published won ____.\" into well-formed sentences. I use large-scale neural language models to compare two classes of models on this task: the task-specific fine-tuning approach standard in AI and NLP, versus an inferential approach involving composition of two simple computational motifs; the inferential approach yields more human-like completions. Third, I show that incorporating hierarchical linguistic structure into one of these computational motifs, namely the auto-regressive word prediction task, yields improvements in neural language model performance on targeted evaluations of models’ grammatical capabilities. I conclude by suggesting future directions in understanding the form and content of these causal generative models of human language."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Cause, Composition, and Structure in Language"]}]}],"canonical_facts":{"dc:contributor.advisor":["Levy, Roger P."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences"],"dc:creator":["Qian, Peng"],"dc:date.accessioned":["2022-09-27T20:16:11Z"],"dc:date.available":["2022-09-27T20:16:11Z"],"dc:date.issued":["2022-05"],"dc:description.abstract":["From everyday communication to exploring new thoughts through writing, humans use language in a remarkably flexible, robust, and creative way. In this thesis, I present three case studies supporting the overarching hypothesis that linguistic knowledge in the human mind can be understood as hierarchically-structured causal generative models, within which a repertoire of compositional inference motifs support efficient inference. I begin with a targeted case study showing how native speakers follow principles of noisy-channel inference in resolving subject-verb agreement mismatches such as \"The gift for the kids are hidden under the bed\". Results suggest that native-speakers' inferences reflect both prior expectations and structure-sensitive conditioning of error probabilities consistent with the statistics of the language production environment. Second, I develop a more open-ended inferential challenge, completing fragmentary linguistic inputs such as \"____ published won ____.\" into well-formed sentences. I use large-scale neural language models to compare two classes of models on this task: the task-specific fine-tuning approach standard in AI and NLP, versus an inferential approach involving composition of two simple computational motifs; the inferential approach yields more human-like completions. Third, I show that incorporating hierarchical linguistic structure into one of these computational motifs, namely the auto-regressive word prediction task, yields improvements in neural language model performance on targeted evaluations of models’ grammatical capabilities. 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