Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities. However, effective aggregation of relevant information in the document remains a challenging research question. Existing approaches construct static document-level graphs based on syntactic trees, co-references or heuristics from the unstructured text to model the dependencies. Unlike previous methods that may not be able to capture rich non-local interactions for inference, we propose a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph. We further develop a refinement strategy, which enables the model to incrementally aggregate relevant information for multi-hop reasoning. Specifically, our model achieves an F 1 score of 59.05 on a large-scale documentlevel dataset (DocRED), significantly improving over the previous results, and also yields new state-of-the-art results on the CDR and GDA dataset. Furthermore, extensive analyses show that the model is able to discover more accurate inter-sentence relations. * * Equally Contributed. † † Work done during internship at SUTD.
Bipolar disorder, an illness characterized by manic and depressive episodes, affects more than 60 million people worldwide. We present a preliminary study on bipolar disorder prediction from user-generated text on Reddit, which relies on users' self-reported labels. Our benchmark classifiers for bipolar disorder prediction outperform the baselines and reach accuracy and F1-scores of above 86%. Feature analysis shows interesting differences in language use between users with bipolar disorders and the control group, including differences in the use of emotion-expressive words.
Mental health poses a significant challenge for an individual's well-being. Text analysis of rich resources, like social media, can contribute to deeper understanding of illnesses and provide means for their early detection. We tackle a challenge of detecting social media users' mental status through deep learningbased models, moving away from traditional approaches to the task. In a binary classification task on predicting if a user suffers from one of nine different disorders, a hierarchical attention network outperforms previously set benchmarks for four of the disorders. Furthermore, we explore the limitations of our model and analyze phrases relevant for classification by inspecting the model's word-level attention weights.
Clarifying an underlying user information need is an important aspect of a modern-day IR system. The importance of clarification is even higher in limited-bandwidth scenarios, such as conversational or mobile search, where a user is unable to easily browse through a long list of retrieved results. Thus, asking clarifying questions about user's potentially ambiguous queries arises as one of the main tasks of conversational search. Recent approaches have, while making significant progress in the field, remained limited to selecting a clarifying question from a predefined set or prompting the user with vague or template-based questions. However, with the recent advances in text generation through large-scale language models, an ideal system should generate the next clarifying question. The challenge of generating an appropriate clarifying question is twofold: (1) to produce the question in coherent natural language;(2) to ask a question that is relevant to the user query. In this paper, we propose a model that generates clarifying questions with respect to the user query and query facets. We fine-tune the GPT-2 language model to generate questions related to the query and one of the extracted query facets. Compared to competitive baselines, results show that our proposed method is both natural and useful, as judged by human annotators. Moreover, we discuss the potential theoretical framework this approach would fit in. We release the code for future work and reproducibility purposes.
CCS CONCEPTS• Computing methodologies → Natural language generation; • Information systems → Search interfaces.
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