| Challenge: | We observe that construction usage lowers the information content of utterances. |
| Approach: | They propose to use construction repetition to mitigate information rate in English open-domain spoken dialogues. |
| Outcome: | The proposed method lowers the information content of utterances, while increasing the frequency and density of repetition. |
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| Challenge: | Repetition of constructions in task-oriented dialogue can have negative and positive effects on information rate and delivery, but is also predictive of task success. |
| Approach: | They investigate the role that efficiency and effectiveness play in speakers’ repetition of shared word sequences, or constructions, in task-oriented dialogue. |
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Linguistically-Informed Specificity and Semantic Plausibility for Dialogue Generation (N19-1)
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| Challenge: | Past work has focused on word frequency-based approaches to improving specificity, such as penalizing responses with only common words. |
| Approach: | They propose to rerank a sequence-to-sequence model to improve the informativeness, reasonableness, and grammatically of responses by using externally-trained classifiers targeting each of these factors. |
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CxGBERT: BERT meets Construction Grammar (2020.coling-main)
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| Challenge: | lexico-semantic elements capture a large amount of linguistic information, but they do not capture all information contained in text. |
| Approach: | They propose to use BERT to train a model that uses a deep bidirectional transformer to capture a significant amount of lexico-semantic information. |
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Constructions are Revealed in Word Distributions (2025.emnlp-main)
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| Challenge: | Construction grammar posits that constructions are form-meaning pairings that are acquired through experience with language. |
| Approach: | They propose to use a RoBERTa model to model constructions as patterns of statistical affinity . they show that statistical affinity is likely an important, but partial, signal available to learners . |
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CoELM: Construction-Enhanced Language Modeling (2024.acl-long)
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| Challenge: | Recent studies show that integrating constructional information can improve the performance of pre-trained language models. |
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BabyLM’s First Constructions: Causal interventions provide a signal of learning (2025.emnlp-main)
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| Challenge: | Recent work shows sensitivity to constructions in pretrained language models, but their relevance to human language learning is doubted. |
| Approach: | They use construction grammars to demonstrate sensitivity to constructions in pretrained language models. |
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Generating Repetitions with Appropriate Repeated Words (2022.naacl-main)
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| Challenge: | Existing studies have focused on general response generation with neural network-based approaches, but none have addressed specific types of repetitions. |
| Approach: | They propose a weighted label smoothing method for explicitly learning which words to repeat during fine-tuning and a repetition scoring method that can output more appropriate repetitions during decoding. |
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Don’t Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training (2020.acl-main)
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| Challenge: | Unlikelihood is a technique developed for removal of repetition in language model completions . it allows for a model to be generalized to solve a number of problems . |
| Approach: | They extend the unlikelihood objective to generate generations that contain repetitions . they show that such an objective can be used to improve logical consistency . |
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A Construction Grammar Corpus of Varying Schematicity: A Dataset for the Evaluation of Abstractions in Language Models (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have been developed without a theoretical framework . evaluating and improving LLMs will benefit from theoretical frameworks that enable comparison of structures of human language and model of language built up by LLM. |
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Dialogue-act-driven Conversation Model : An Experimental Study (C18-1)
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| Challenge: | In the last decade, natural language processing and machine learning have come a long way towards building an automated dialogue system. |
| Approach: | They propose a way to encode dialogue act information and use it to build a model that can use it in a natural way. |
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