Bilingual Rhetorical Structure Parsing with Large Parallel Annotations

• URL: http://arxiv.org/abs/2409.14969v1
• Date: Mon, 23 Sep 2024 12:40:33 GMT
• Title: Bilingual Rhetorical Structure Parsing with Large Parallel Annotations
• Authors: Elena Chistova,
• Abstract summary: We introduce a parallel Russian annotation for the large and diverse English GUM RST corpus. Our end-to-end RST achieves state-of-the-art results on both English and Russian corpora. To the best of our knowledge, this work is the first to evaluate the potential of cross-lingual end-to-end RST parsing on a manually annotated parallel corpus.
• Score: 5.439020425819001
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Discourse parsing is a crucial task in natural language processing that aims to reveal the higher-level relations in a text. Despite growing interest in cross-lingual discourse parsing, challenges persist due to limited parallel data and inconsistencies in the Rhetorical Structure Theory (RST) application across languages and corpora. To address this, we introduce a parallel Russian annotation for the large and diverse English GUM RST corpus. Leveraging recent advances, our end-to-end RST parser achieves state-of-the-art results on both English and Russian corpora. It demonstrates effectiveness in both monolingual and bilingual settings, successfully transferring even with limited second-language annotation. To the best of our knowledge, this work is the first to evaluate the potential of cross-lingual end-to-end RST parsing on a manually annotated parallel corpus.

Related papers

• Enhancing Cross-lingual Transfer via Phonemic Transcription Integration [57.109031654219294]
  PhoneXL is a framework incorporating phonemic transcriptions as an additional linguistic modality for cross-lingual transfer. Our pilot study reveals phonemic transcription provides essential information beyond the orthography to enhance cross-lingual transfer.
  arXiv  Detail & Related papers  (2023-07-10T06:17:33Z)
• VECO 2.0: Cross-lingual Language Model Pre-training with Multi-granularity Contrastive Learning [56.47303426167584]
  We propose a cross-lingual pre-trained model VECO2.0 based on contrastive learning with multi-granularity alignments. Specifically, the sequence-to-sequence alignment is induced to maximize the similarity of the parallel pairs and minimize the non-parallel pairs. token-to-token alignment is integrated to bridge the gap between synonymous tokens excavated via the thesaurus dictionary from the other unpaired tokens in a bilingual instance.
  arXiv  Detail & Related papers  (2023-04-17T12:23:41Z)
• Advancing Multilingual Pre-training: TRIP Triangular Document-level Pre-training for Multilingual Language Models [107.83158521848372]
  We present textbfTriangular Document-level textbfPre-training (textbfTRIP), which is the first in the field to accelerate the conventional monolingual and bilingual objectives into a trilingual objective with a novel method called Grafting. TRIP achieves several strong state-of-the-art (SOTA) scores on three multilingual document-level machine translation benchmarks and one cross-lingual abstractive summarization benchmark, including consistent improvements by up to 3.11 d-BLEU points and 8.9 ROUGE-L points.
  arXiv  Detail & Related papers  (2022-12-15T12:14:25Z)
• DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing [24.986030179701405]
  We propose a document-level multilingual RST discourse parsing framework, which conducts EDU segmentation and discourse tree parsing jointly. Our model achieves state-of-the-art performance on document-level multilingual RST parsing in all sub-tasks.
  arXiv  Detail & Related papers  (2021-10-09T09:15:56Z)
• Syntax-augmented Multilingual BERT for Cross-lingual Transfer [37.99210035238424]
  This work shows that explicitly providing language syntax and training mBERT helps cross-lingual transfer. Experiment results show that syntax-augmented mBERT improves cross-lingual transfer on popular benchmarks.
  arXiv  Detail & Related papers  (2021-06-03T21:12:50Z)
• Multilingual Neural RST Discourse Parsing [24.986030179701405]
  We investigate two approaches to establish a neural, cross-lingual discourse via multilingual vector representations and segment-level translation. Experiment results show that both methods are effective even with limited training data, and achieve state-of-the-art performance on cross-lingual, document-level discourse parsing.
  arXiv  Detail & Related papers  (2020-12-03T05:03:38Z)
• Unleashing the Power of Neural Discourse Parsers -- A Context and Structure Aware Approach Using Large Scale Pretraining [26.517219486173598]
  RST-based discourse parsing is an important NLP task with numerous downstream applications, such as summarization, machine translation and opinion mining. In this paper, we demonstrate a simple, yet highly accurate discourse parsing, incorporating recent contextual language models. Our establishes the new state-of-the-art (SOTA) performance for predicting structure and nuclearity on two key RST datasets, RST-DT and Instr-DT.
  arXiv  Detail & Related papers  (2020-11-06T06:11:26Z)
• VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation [77.82373082024934]
  We plug a cross-attention module into the Transformer encoder to explicitly build the interdependence between languages. It can effectively avoid the degeneration of predicting masked words only conditioned on the context in its own language. The proposed cross-lingual model delivers new state-of-the-art results on various cross-lingual understanding tasks of the XTREME benchmark.
  arXiv  Detail & Related papers  (2020-10-30T03:41:38Z)
• On the Language Neutrality of Pre-trained Multilingual Representations [70.93503607755055]
  We investigate the language-neutrality of multilingual contextual embeddings directly and with respect to lexical semantics. Our results show that contextual embeddings are more language-neutral and, in general, more informative than aligned static word-type embeddings. We show how to reach state-of-the-art accuracy on language identification and match the performance of statistical methods for word alignment of parallel sentences.
  arXiv  Detail & Related papers  (2020-04-09T19:50:32Z)
• Robust Cross-lingual Embeddings from Parallel Sentences [65.85468628136927]
  We propose a bilingual extension of the CBOW method which leverages sentence-aligned corpora to obtain robust cross-lingual word representations. Our approach significantly improves crosslingual sentence retrieval performance over all other approaches. It also achieves parity with a deep RNN method on a zero-shot cross-lingual document classification task.
  arXiv  Detail & Related papers  (2019-12-28T16:18:33Z)

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.