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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
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In: Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021) ; https://hal.archives-ouvertes.fr/hal-03466171 ; Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), Aug 2021, Online, France. pp.96-120, ⟨10.18653/v1/2021.gem-1.10⟩ (2021)
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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics ...
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Towards Syntax-Aware DialogueSummarization using Multi-task Learning ...
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Who speaks like a style of Vitamin: Towards Syntax-Aware DialogueSummarization using Multi-task Learning ...
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Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders
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In: NPJ Schizophr (2021)
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Measuring the `I don't know' Problem through the Lens of Gricean Quantity ...
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SMRT Chatbots: Improving Non-Task-Oriented Dialog with Simulated Multiple Reference Training ...
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Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification ...
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Comparison of Diverse Decoding Methods from Conditional Language Models ...
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Learning Word Ratings for Empathy and Distress from Document-Level User Responses ...
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Abstract:
Despite the excellent performance of black box approaches to modeling sentiment and emotion, lexica (sets of informative words and associated weights) that characterize different emotions are indispensable to the NLP community because they allow for interpretable and robust predictions. Emotion analysis of text is increasing in popularity in NLP; however, manually creating lexica for psychological constructs such as empathy has proven difficult. This paper automatically creates empathy word ratings from document-level ratings. The underlying problem of learning word ratings from higher-level supervision has to date only been addressed in an ad hoc fashion and has not used deep learning methods. We systematically compare a number of approaches to learning word ratings from higher-level supervision against a Mixed-Level Feed Forward Network (MLFFN), which we find performs best, and use the MLFFN to create the first-ever empathy lexicon. We then use Signed Spectral Clustering to gain insights into the resulting ... : LREC 2020 camera-ready copy ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Information Retrieval cs.IR
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URL: https://arxiv.org/abs/1912.01079 https://dx.doi.org/10.48550/arxiv.1912.01079
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Unsupervised Post-processing of Word Vectors via Conceptor Negation ...
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