Discourse and Document-level Information for Evaluating Language Output Tasks
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationSimilar Papers 제목 키워드 기반
A Test Suite for Evaluating Discourse Phenomena in Document-level Neural Machine Translation
The need to evaluate the ability of context-aware neural machine translation (NMT) models in dealing with specific discourse phenomena arises in document-level NMT. However, test sets that satisfy this need are rare. In …
Machine TranslationNMTTranslationMultilingual Neural RST Discourse Parsing
Text discourse parsing plays an important role in understanding information flow and argumentative structure in natural language. Previous research under the Rhetorical Structure Theory (RST) has mostly focused on induci…
Discourse ParsingTranslationDocument-Level Machine Translation with Large Language Models
Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing (NLP) tasks. Taking document-level machine translation (MT) as a testbed, …
Document Level Machine TranslationMachine TranslationTranslationBeDiscovER: The Benchmark of Discourse Understanding in the Era of Reasoning Language Models
We introduce BeDiscovER (Benchmark of Discourse Understanding in the Era of Reasoning Language Models), an up-to-date, comprehensive suite for evaluating the discourse-level knowledge of modern LLMs. BeDiscovER compiles …
Temporal Relation ExtractionRelation ClassificationDiscourse ParsingCross-lingual RST Discourse Parsing
Discourse parsing is an integral part of understanding information flow and argumentative structure in documents. Most previous research has focused on inducing and evaluating models from the English RST Discourse Treeba…
Discourse Parsing