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<?xml version="1.0"?><rss version="2.0" xmlns:prism="http://purl.org/rss/1.0/modules/prism/"> <channel> <title>Computational Linguistics Advance Access</title> <link>https://direct.mit.edu/coli</link> <description> </description> <language>en-us</language> <pubDate>Thu, 19 Jun 2025 00:00:00 GMT</pubDate> <lastBuildDate>Fri, 20 Jun 2025 22:45:13 GMT</lastBuildDate> <generator>Silverchair</generator> <managingEditor>editor@direct.mit.edu/coli</managingEditor> <webMaster>webmaster@direct.mit.edu/coli</webMaster> <item> <title>UniASA: A Unified Generative Framework for Argument Structure Analysis</title> <link>https://direct.mit.edu/coli/article/doi/10.1162/coli_a_00553/127893/UniASA-A-Unified-Generative-Framework-for-Argument</link> <pubDate>Thu, 19 Jun 2025 00:00:00 GMT</pubDate> <description><span class="paragraphSection"><div class="boxTitle">Abstract</div>Argumentation is a fundamental human activity that involves reasoning and persuasion, which also serves as the basis for the development of AI systems capable of complex reasoning. In NLP, to better understand human argumentation, argument structure analysis aims to identify argument components, such as claims and premises, and their relations from free text. It encompasses a variety of divergent tasks, such as end-to-end argument mining, argument pair extraction, and argument quadruplet extraction. Existing methods are usually tailored to only one specific argument structure analysis task, overlooking the inherent connections among different tasks. We observe that the fundamental goal of these tasks is similar: identifying argument components and their interrelations. Motivated by this, we present a unified generative framework for argument structure analysis (UniASA). It can uniformly address multiple argument structure analysis tasks in a sequence-to-sequence manner. Further, we enhance UniASA with a multi-view learning strategy based on subtask decomposition. We conduct experiments on seven datasets across three tasks. The results indicate that UniASA can address these tasks uniformly and achieve performance that is either superior to or comparable with the previous state-of-the-art methods. Also, we show that UniASA can be effectively integrated with large language models, such as Llama, through fine-tuning or in-context learning.</span></description> <prism:startingPage xmlns:prism="prism">1</prism:startingPage> <prism:endingPage xmlns:prism="prism">46</prism:endingPage> <prism:doi xmlns:prism="prism">10.1162/coli_a_00553</prism:doi> <guid>https://direct.mit.edu/coli/article/doi/10.1162/coli_a_00553/127893/UniASA-A-Unified-Generative-Framework-for-Argument</guid> </item> <item> <title>The Emergence of Chunking Structures with Hierarchical RNN</title> <link>https://direct.mit.edu/coli/article/doi/10.1162/coli_a_00545/125288/The-Emergence-of-Chunking-Structures-with</link> <pubDate>Thu, 19 Jun 2025 00:00:00 GMT</pubDate> <description><span class="paragraphSection"><div class="boxTitle">Abstract</div>In Natural Language Processing (NLP), predicting linguistic structures, such as parsing and chunking, has mostly relied on manual annotations of syntactic structures. This article introduces an unsupervised approach to chunking, a syntactic task that involves grouping words in a non-hierarchical manner. We present a Hierarchical Recurrent Neural Network (HRNN) designed to model word-to-chunk and chunk-to-sentence compositions. Our approach involves a two-stage training process: pretraining with an unsupervised parser and finetuning on downstream NLP tasks. Experiments on multiple datasets reveal a notable improvement of unsupervised chunking performance in both pretraining and finetuning stages. Interestingly, we observe that the emergence of the chunking structure is transient during the neural model’s downstream-task training. This study contributes to the advancement of unsupervised syntactic structure discovery and opens avenues for further research in linguistic theory.<sup>1</sup></span></description> <prism:startingPage xmlns:prism="prism">1</prism:startingPage> <prism:endingPage xmlns:prism="prism">27</prism:endingPage> <prism:doi xmlns:prism="prism">10.1162/coli_a_00545</prism:doi> <guid>https://direct.mit.edu/coli/article/doi/10.1162/coli_a_00545/125288/The-Emergence-of-Chunking-Structures-with</guid> </item> <item> <title>Exploiting Contextual Embeddings in Hierarchical Topic Modeling and Investigating the Limits of the Current Evaluation Metrics</title> <link>https://direct.mit.edu/coli/article/doi/10.1162/coli_a_00543/124885/Exploiting-Contextual-Embeddings-in-Hierarchical</link> <pubDate>Thu, 19 Jun 2025 00:00:00 GMT</pubDate> <description><span class="paragraphSection"><div class="boxTitle">Abstract</div>We investigate two essential challenges in the context of Hierarchical Topic Modeling (HTM)—(i) the impact of data representation and (ii) topic evaluation. The data representation directly influences the performance of the topic generation, and the impact of new representations such as contextual embeddings in this task has been under-investigated. Topic evaluation, responsible for driving the advances in the field, assesses the overall quality of the topic generation process. HTM studies exploit the exact topic modeling (TM) evaluation metrics as traditional TM to measure the quality of topics. One significant result of our work is demonstrating that the HTM’s hierarchical nature demands novel ways of evaluating the quality of topics. As our main contribution, we propose two new topic quality metrics to assess the topical quality of the hierarchical structures. Uniqueness considers topic topological consistency, while the Semantic Hierarchical Structure (SHS) captures the semantic relatedness of the hierarchies. We also present an additional advance to the state-of-the-art by proposing the c-CluHTM. To the best of our knowledge, c-CluHTM is the first method that exploits contextual embeddings into NMF in HTM tasks. c-CluHTM enhances the topics’ semantics while preserving the hierarchical structure. We perform an experimental evaluation, and our results demonstrate the superiority of our proposal with gains between 12% and 21%, regarding NPMI and Coherence over the best baselines. Regarding the newly proposed metrics, our results reveal that Uniqueness and SHS can capture relevant information about the structure of the hierarchical topics that traditional metrics cannot.</span></description> <prism:startingPage xmlns:prism="prism">1</prism:startingPage> <prism:endingPage xmlns:prism="prism">41</prism:endingPage> <prism:doi xmlns:prism="prism">10.1162/coli_a_00543</prism:doi> <guid>https://direct.mit.edu/coli/article/doi/10.1162/coli_a_00543/124885/Exploiting-Contextual-Embeddings-in-Hierarchical</guid> </item> <item> <title>A Novel Methodology for Enhancing Cross-language and Domain Adaptability in Temporal Expression Normalization</title> <link>https://direct.mit.edu/coli/article/doi/10.1162/COLI.a.12/130701/A-Novel-Methodology-for-Enhancing-Cross-language</link> <pubDate>Wed, 11 Jun 2025 00:00:00 GMT</pubDate> <description><span class="paragraphSection"><div class="boxTitle">Abstract</div>Accurate temporal expression normalization, the process of assigning a numerical value to a temporal expression, is essential for tasks such as timeline creation and temporal reasoning. While rule-based normalization systems are limited in adaptability across different domains and languages, deep-learning solutions in this area have not been extensively explored. An additional challenge is the scarcity of manually annotated corpora with temporal annotations. To address the adaptability limitations of current systems, we propose a highly adaptable methodology that can be applied to multiple domains and languages. This can be achieved by leveraging a multilingual Pre-trained Language Model (PTLM) with a fill-mask architecture, using a Value Intermediate Representation (VIR) where the temporal expression value format is adjusted to the fill-mask representation. Our approach involves a two-phase training process. Initially, the model is trained with a novel masking policy on a large English biomedical corpus that is automatically annotated with normalized temporal expressions, along with a complementary hand-crafted temporal expressions corpus. This addresses the lack of manually annotated data and helps to achieve sufficient capacity for adaptation to diverse domains or languages. In the second phase, we show how the model can be tailored to different domains and languages using various techniques, showcasing the versatility of the proposed methodology. This approach significantly outperforms existing systems.</span></description> <prism:startingPage xmlns:prism="prism">1</prism:startingPage> <prism:endingPage xmlns:prism="prism">33</prism:endingPage> <prism:doi xmlns:prism="prism">10.1162/COLI.a.12</prism:doi> <guid>https://direct.mit.edu/coli/article/doi/10.1162/COLI.a.12/130701/A-Novel-Methodology-for-Enhancing-Cross-language</guid> </item> </channel></rss>If you would like to create a banner that links to this page (i.e. this validation result), do the following:
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