Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank
Detecting fine-grained differences in content conveyed in different languages matters for cross-lingual NLP and multilingual corpora analysis, but it is a challenging machine learning problem since annotation is expensive and hard to scale. This work improves the prediction and annotation of fine-grained semantic divergences. We introduce a training strategy for multilingual BERT models by learning to rank synthetic divergent examples of varying granularity. We evaluate our models on the Rationalized English-French Semantic Divergences, a new dataset released with this work, consisting of English-French sentence-pairs annotated with semantic divergence classes and token-level rationales. Learning to rank helps detect fine-grained sentence-level divergences more accurately than a strong sentence-level similarity model, while token-level predictions have the potential of further distinguishing between coarse and fine-grained divergences.
Code (1)
Tasks
Learning-To-RankSentenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representation
Identifying semantically equivalent sentences is important for many cross-lingual and mono-lingual NLP tasks. Current approaches to semantic equivalence take a loose, sentence-level approach to "equivalence," despite pre…
Abstract Meaning RepresentationMachine TranslationSemantic SimilaritySemantic Textual Similarity+2Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples
The evaluation of cross-lingual semantic search capabilities of models is often limited to existing datasets from tasks such as information retrieval and semantic textual similarity. To allow for domain-specific evaluati…
Distractor GenerationInformation RetrievalLanguage ModelingLanguage Modelling+4Towards Universal Semantic Tagging
The paper proposes the task of universal semantic tagging---tagging word tokens with language-neutral, semantically informative tags. We argue that the task, with its independent nature, contributes to better semantic an…
Semantic ParsingGloCTM: Cross-Lingual Topic Modeling via a Global Context Space
Cross-lingual topic modeling seeks to uncover coherent and semantically aligned topics across languages - a task central to multilingual understanding. Yet most existing models learn topics in disjoint, language-specific…
FrameNet and Typology
FrameNet and the Multilingual FrameNet project have produced multilingual semantic annotations of parallel texts that yield extremely fine-grained typological insights. Moreover, frame semantic annotation of a wide cross…
Diversity