X-PARADE: Cross-Lingual Textual Entailment and Information Divergence across Paragraphs

• URL: http://arxiv.org/abs/2309.08873v2
• Date: Mon, 15 Apr 2024 18:39:01 GMT
• Title: X-PARADE: Cross-Lingual Textual Entailment and Information Divergence across Paragraphs
• Authors: Juan Diego Rodriguez, Katrin Erk, Greg Durrett,
• Abstract summary: X-PARADE is the first cross-lingual dataset of paragraph-level information divergences. Annotators label a paragraph in a target language at the span level and evaluate it with respect to a corresponding paragraph in a source language. Aligned paragraphs are sourced from Wikipedia pages in different languages.
• Score: 55.80189506270598
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding when two pieces of text convey the same information is a goal touching many subproblems in NLP, including textual entailment and fact-checking. This problem becomes more complex when those two pieces of text are in different languages. Here, we introduce X-PARADE (Cross-lingual Paragraph-level Analysis of Divergences and Entailments), the first cross-lingual dataset of paragraph-level information divergences. Annotators label a paragraph in a target language at the span level and evaluate it with respect to a corresponding paragraph in a source language, indicating whether a given piece of information is the same, new, or new but can be inferred. This last notion establishes a link with cross-language NLI. Aligned paragraphs are sourced from Wikipedia pages in different languages, reflecting real information divergences observed in the wild. Armed with our dataset, we investigate a diverse set of approaches for this problem, including token alignment from machine translation, textual entailment methods that localize their decisions, and prompting LLMs. Our results show that these methods vary in their capability to handle inferable information, but they all fall short of human performance.

Related papers

• Understanding Cross-Lingual Alignment -- A Survey [52.572071017877704]
  Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. We survey the literature of techniques to improve cross-lingual alignment, providing a taxonomy of methods and summarising insights from throughout the field.
  arXiv  Detail & Related papers  (2024-04-09T11:39:53Z)
• Automatic Data Retrieval for Cross Lingual Summarization [4.759360739268894]
  Cross-lingual summarization involves the summarization of text written in one language to a different one. In this work, we aim to perform cross-lingual summarization from English to Hindi.
  arXiv  Detail & Related papers  (2023-12-22T09:13:24Z)
• Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction [38.73550742775257]
  A source query in one language may yield several translation options in another language without any extra context. We propose a novel method interactive-chain prompting that reduces translations into a list of subproblems addressing ambiguities. We create a dataset exhibiting different linguistic phenomena which leads to ambiguities at inference for four languages.
  arXiv  Detail & Related papers  (2023-01-24T21:08:13Z)
• Understanding Translationese in Cross-Lingual Summarization [106.69566000567598]
  Cross-lingual summarization (MS) aims at generating a concise summary in a different target language. To collect large-scale CLS data, existing datasets typically involve translation in their creation. In this paper, we first confirm that different approaches of constructing CLS datasets will lead to different degrees of translationese.
  arXiv  Detail & Related papers  (2022-12-14T13:41:49Z)
• CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval [73.48591773882052]
  Most fact-checking approaches focus on English only due to the data scarcity issue in other languages. We present the first fact-checking framework augmented with crosslingual retrieval. We train the retriever with our proposed Crosslingual Inverse Cloze Task (XICT)
  arXiv  Detail & Related papers  (2022-09-05T17:36:14Z)
• Exposing Cross-Lingual Lexical Knowledge from Multilingual Sentence Encoders [85.80950708769923]
  We probe multilingual language models for the amount of cross-lingual lexical knowledge stored in their parameters, and compare them against the original multilingual LMs. We also devise a novel method to expose this knowledge by additionally fine-tuning multilingual models. We report substantial gains on standard benchmarks.
  arXiv  Detail & Related papers  (2022-04-30T13:23:16Z)
• FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding [85.29270319872597]
  We propose an enhanced fusion method that takes cross-lingual data as input for XLM finetuning. During inference, the model makes predictions based on the text input in the target language and its translation in the source language. To tackle this issue, we propose an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language.
  arXiv  Detail & Related papers  (2020-09-10T22:42:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.