Semantics of a sentence is defined with much less ambiguity than semantics of a single word, and we assume that it should be better preserved by translation to another language. If multilingual sentence embeddings intend to represent sentence semantics, then the similarity between embeddings of any two sentences must be invariant with respect to translation. Based on this suggestion, we consider a simple linear cross-lingual mapping as a possible improvement of the multilingual embeddings. We also consider deviation from orthogonality conditions as a measure of deficiency of the embeddings.

Linear Cross-Lingual Mapping of Sentence Embeddings

We propose a new reference-free summary quality evaluation measure, with emphasis on the faithfulness. The measure is based on finding and counting all probable potential inconsistencies of the summary with respect to the source document. The proposed ESTIME, Estimator of Summary-to-Text Inconsistency by Mismatched Embeddings, correlates with expert scores in summary-level SummEval dataset stronger than other common evaluation measures not only in Consistency but also in Fluency. We also introduce a method of generating subtle factual errors in human summaries. We show that ESTIME is more sensitive to subtle errors than other common evaluation measures.

ESTIME: Estimation of Summary-to-Text Inconsistency by Mismatched Embeddings

A summary quality measure is judged by how well it correlates with quality scores produced by human annotators. A higher correlation with human scores is considered to be a decisive indicator of a better measure. In this work we present observations that cast doubt on this view. We also show a possibility of an alternative indicator for selecting the best measure from a family of measures, a criterion that does not rely on human scores.

Is Human Scoring the Best Criteria for Summary Evaluation?

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Virtual Poster Session (asynchronous viewing)

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ACL 2024

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W5: Evaluation & Comparison of NLP Systems (Eval4NLP)

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Findings Papers - Resource and Evaluation

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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Oleg Vasilyev

4

SHORT BIO

Presentations

Linear Cross-Lingual Mapping of Sentence Embeddings

ESTIME: Estimation of Summary-to-Text Inconsistency by Mismatched Embeddings

Is Human Scoring the Best Criteria for Summary Evaluation?

Is Human Scoring the Best Criteria for Summary Evaluation?

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