Васерштейн қашықтығы

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Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractWord embeddings have become widely-used in document analysis. While a large number of models for mapping words to vector spaces have been developed, it remains undetermined how much net gain …...

36993

LCQMC:A Large-scale Chinese Question Matching Corpus

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractThe lack of large-scale question matching corpora greatly limits the development of matching methods in question answering (QA) system, especially for non-English languages. To ameliorate this situation, in this paper, …...

5351

Wasserstein Distance Regularized Sequence Representation for Text Matching in Asymmetrical Domains

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractOne approach to matching texts from asymmetrical domains is projecting the input sequences into a common semantic space as feature vectors upon which the matching function can be readily defined …...

30591

Unsupervised Opinion Summarisation in the Wasserstein Space

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractOpinion summarisation synthesises opinions expressed in a group of documents discussingthe same topic to produce a single summary. Recent work has looked at opinion summarisation of clusters of social media …...

30298

Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractIn this paper, we cast Legal Judgment Prediction on European Court of Human Rights cases into an article-aware classification task, where the case outcome is classified from a combined input …...

33406