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1. Parsing with Multilingual BERT, a Small Corpus, and a Small Treebank

   https://doi.org/10.18653/v1/2020.findings-emnlp.118  

   Chau, Ethan C.; Lin, Lucy H.; Smith, Noah A. (January 2020, : Findings of the Association for Computational Linguistics: EMNLP 2020)

   Pretrained multilingual contextual representations have shown great success, but due to the limits of their pretraining data, their benefits do not apply equally to all language varieties. This presents a challenge for language varieties unfamiliar to these models, whose labeled and unlabeled data is too limited to train a monolingual model effectively. We propose the use of additional language-specific pretraining and vocabulary augmentation to adapt multilingual models to low-resource settings. Using dependency parsing of four diverse low-resource language varieties as a case study, we show that these methods significantly improve performance over baselines, especially in the lowest-resource cases, and demonstrate the importance of the relationship between such models’ pretraining data and target language varieties. 

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2. Dense Paraphrasing for multimodal dialogue interpretation

   https://doi.org/10.3389/frai.2024.1479905  

   Tu, Jingxuan; Rim, Kyeongmin; Ye, Bingyang; Lai, Kenneth; Pustejovsky, James (December 2024, Frontiers in Artificial Intelligence)

   Multimodal dialogue involving multiple participants presents complex computational challenges, primarily due to the rich interplay of diverse communicative modalities including speech, gesture, action, and gaze. These modalities interact in complex ways that traditional dialogue systems often struggle to accurately track and interpret. To address these challenges, we extend the textual enrichment strategy of Dense Paraphrasing (DP), by translating each nonverbal modality into linguistic expressions. By normalizing multimodal information into a language-based form, we hope to both simplify the representation for and enhance the computational understanding of situated dialogues. We show the effectiveness of the dense paraphrased language form by evaluating instruction-tuned Large Language Models (LLMs) against the Common Ground Tracking (CGT) problem using a publicly available collaborative problem-solving dialogue dataset. Instead of using multimodal LLMs, the dense paraphrasing technique represents the dialogue information from multiple modalities in a compact and structured machine-readable text format that can be directly processed by the language-only models. We leverage the capability of LLMs to transform machine-readable paraphrases into human-readable paraphrases, and show that this process can further improve the result on the CGT task. Overall, the results show that augmenting the context with dense paraphrasing effectively facilitates the LLMs' alignment of information from multiple modalities, and in turn largely improves the performance of common ground reasoning over the baselines. Our proposed pipeline with original utterances as input context already achieves comparable results to the baseline that utilized decontextualized utterances which contain rich coreference information. When also using the decontextualized input, our pipeline largely improves the performance of common ground reasoning over the baselines. We discuss the potential of DP to create a robust model that can effectively interpret and integrate the subtleties of multimodal communication, thereby improving dialogue system performance in real-world settings. 

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3. Neural network vs. HMM speech recognition systems as models of human cross-linguistic phonetic perception

   Schatz, Thomas; Feldman, Naomi H. (September 2018, Proceedings of the Conference on Cognitive Computational Neuroscience)

   The way listeners perceive speech sounds is largely determined by the language(s) they were exposed to as a child. For example, native speakers of Japanese have a hard time discriminating between American English /ɹ/ and /l/, a phonetic contrast that has no equivalent in Japanese. Such effects are typically attributed to knowledge of sounds in the native language, but quantitative models of how these effects arise from linguistic knowledge are lacking. One possible source for such models is Automatic Speech Recognition (ASR) technology. We implement models based on two types of systems from the ASR literature—hidden Markov models (HMMs) and the more recent, and more accurate, neural network systems—and ask whether, in addition to showing better performance, the neural network systems also provide better models of human perception. We find that while both types of systems can account for Japanese natives’ difficulty with American English /ɹ/ and /l/, only the neural network system successfully accounts for Japanese natives’ facility with Japanese vowel length contrasts. Our work provides a new example, in the domain of speech perception, of an often observed correlation between task performance and similarity to human behavior. 

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4. Clinically relevant pretraining is all you need

   https://doi.org/10.1093/jamia/ocab086  

   Bear_Don’t_Walk_IV, Oliver J; Sun, Tony; Perotte, Adler; Elhadad, Noémie (June 2021, Journal of the American Medical Informatics Association)

   Abstract Clinical notes present a wealth of information for applications in the clinical domain, but heterogeneity across clinical institutions and settings presents challenges for their processing. The clinical natural language processing field has made strides in overcoming domain heterogeneity, while pretrained deep learning models present opportunities to transfer knowledge from one task to another. Pretrained models have performed well when transferred to new tasks; however, it is not well understood if these models generalize across differences in institutions and settings within the clinical domain. We explore if institution or setting specific pretraining is necessary for pretrained models to perform well when transferred to new tasks. We find no significant performance difference between models pretrained across institutions and settings, indicating that clinically pretrained models transfer well across such boundaries. Given a clinically pretrained model, clinical natural language processing researchers may forgo the time-consuming pretraining step without a significant performance drop. 

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5. Generating Frequently Asked Questions from Technical Support Tickets using Large Language Models

   https://doi.org/10.1145/3731599.3767429  

   Joslin, Christina; Burns, David; Ashish, Ashish; Barezi, Elham J (November 2025, HUST SC Workshops ’25, November 16–21, 2025, St Louis, MO, USA)

   High-performance computing is a driving force behind scientific innovation and discovery. However, as the number of users and the complexity of high-performance computing systems grow, so does the volume and variability of technical issues handled by sup- port teams. The evolving nature of these issues presents a need for automated tools that can extract clear, accurate, and relevant fre- quently asked questions directly from support tickets. This need was addressed by developing a novel pipeline that incorporates seman- tic clustering, representation learning, and large language models. While prior research laid strong foundations across classification, clustering and large language model-based questions & answers, our work augments these efforts by integrating semantic clustering, domain-specific summarization, and multi-stage generation into a scalable pipeline for autonomous technical support. To prioritize high-impact issues, the pipeline began by filtering tickets based on anomaly frequency and recency. It then leveraged an instruction- tuned large language model to clean and summarize each ticket into a structured issue-resolution pair. Next, unsupervised semantic clus- tering was performed to identify subclusters of semantically similar tickets within broader topic clusters. A large language model-based generation module was then applied to create frequently asked questions representing the most dominant issues. A structured evaluation by subject matter experts indicated that our approach transformed technical support tickets into understandable, factu- ally sound, and pertinent frequently asked questions. The ability to extract fine-grained insights from raw ticket data enhances the scalability, efficiency, and responsiveness of technical support work- flows in high-performance computing environments, ultimately enabling faster troubleshooting and more accessible pathways to scientific discovery. 

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