Test-Time Code-Switching for Cross-lingual Aspect Sentiment Triplet Extraction

• URL: http://arxiv.org/abs/2501.14144v1
• Date: Fri, 24 Jan 2025 00:00:51 GMT
• Title: Test-Time Code-Switching for Cross-lingual Aspect Sentiment Triplet Extraction
• Authors: Dongming Sheng, Kexin Han, Hao Li, Yan Zhang, Yucheng Huang, Jun Lang, Wenqiang Liu,
• Abstract summary: We introduce a novel Test-Time Code-SWitching (TT-CSW) framework to bridge the gap between the bilingual training phase and the monolingual test-time prediction. During training, a generative model is developed based on bilingual code-switched training data and can produce bilingual ASTE triplets for bilingual inputs. In the testing stage, we employ an alignment-based code-switching technique for test-time augmentation.
• Score: 12.269762062755492
• License:
• Abstract: Aspect Sentiment Triplet Extraction (ASTE) is a thriving research area with impressive outcomes being achieved on high-resource languages. However, the application of cross-lingual transfer to the ASTE task has been relatively unexplored, and current code-switching methods still suffer from term boundary detection issues and out-of-dictionary problems. In this study, we introduce a novel Test-Time Code-SWitching (TT-CSW) framework, which bridges the gap between the bilingual training phase and the monolingual test-time prediction. During training, a generative model is developed based on bilingual code-switched training data and can produce bilingual ASTE triplets for bilingual inputs. In the testing stage, we employ an alignment-based code-switching technique for test-time augmentation. Extensive experiments on cross-lingual ASTE datasets validate the effectiveness of our proposed method. We achieve an average improvement of 3.7% in terms of weighted-averaged F1 in four datasets with different languages. Additionally, we set a benchmark using ChatGPT and GPT-4, and demonstrate that even smaller generative models fine-tuned with our proposed TT-CSW framework surpass ChatGPT and GPT-4 by 14.2% and 5.0% respectively.

Related papers

• What Is Missing in Multilingual Visual Reasoning and How to Fix It [57.37123046817781]
  We evaluate NLP models' multilingual, multimodal capabilities by testing on a visual reasoning task. proprietary systems like GPT-4V obtain the best performance on this task now, but open models lag in comparison. Our interventions achieve the best open performance on this task in a zero-shot setting, boosting open models LLaVA-v1.5-13B by 13.4%, LLaVA-v1.6-34B by 20.3%, and Qwen-VL by 16.7%.
  arXiv  Detail & Related papers  (2024-03-03T05:45:27Z)
• Translation-Enhanced Multilingual Text-to-Image Generation [61.41730893884428]
  Research on text-to-image generation (TTI) still predominantly focuses on the English language. In this work, we thus investigate multilingual TTI and the current potential of neural machine translation (NMT) to bootstrap mTTI systems. We propose Ensemble Adapter (EnsAd), a novel parameter-efficient approach that learns to weigh and consolidate the multilingual text knowledge within the mTTI framework.
  arXiv  Detail & Related papers  (2023-05-30T17:03:52Z)
• LLM-powered Data Augmentation for Enhanced Cross-lingual Performance [24.20730298894794]
  This paper explores the potential of leveraging Large Language Models (LLMs) for data augmentation in commonsense reasoning datasets. To achieve this, we utilise several LLMs, namely Dolly-v2, StableVicuna, ChatGPT, and GPT-4, to augment three datasets: XCOPA, XWinograd, and XStoryCloze. We evaluate the effectiveness of fine-tuning smaller multilingual models, mBERT and XLMR, using the synthesised data.
  arXiv  Detail & Related papers  (2023-05-23T17:33:27Z)
• 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)
• Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation [80.16548523140025]
  We extend the vanilla pretrain-finetune pipeline with extra code-switching restore task to bridge the gap between the pretrain and finetune stages. Our approach could narrow the cross-lingual sentence representation distance and improve low-frequency word translation with trivial computational cost.
  arXiv  Detail & Related papers  (2022-04-16T16:08:38Z)
• The Impact of Cross-Lingual Adjustment of Contextual Word Representations on Zero-Shot Transfer [3.300216758849348]
  Large multilingual language models such as mBERT or XLM-R enable zero-shot cross-lingual transfer in various IR and NLP tasks. We propose a data- and compute-efficient method for cross-lingual adjustment of mBERT that uses a small parallel corpus to make embeddings of related words across languages similar to each other. We experiment with a typologically diverse set of languages (Spanish, Russian, Vietnamese, and Hindi) and extend their original implementations to new tasks. Our study reproduced gains in NLI for four languages, showed improved NER, XSR, and cross-lingual QA
  arXiv  Detail & Related papers  (2022-04-13T15:28:43Z)
• 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)
• ZmBART: An Unsupervised Cross-lingual Transfer Framework for Language Generation [4.874780144224057]
  Cross-lingual transfer for natural language generation is relatively understudied. We consider four NLG tasks (text summarization, question generation, news headline generation, and distractor generation) and three syntactically diverse languages. We propose an unsupervised cross-lingual language generation framework (called ZmBART) that does not use any parallel or pseudo-parallel/back-translated data.
  arXiv  Detail & Related papers  (2021-06-03T05:08:01Z)
• Explicit Alignment Objectives for Multilingual Bidirectional Encoders [111.65322283420805]
  We present a new method for learning multilingual encoders, AMBER (Aligned Multilingual Bi-directional EncodeR) AMBER is trained on additional parallel data using two explicit alignment objectives that align the multilingual representations at different granularities. Experimental results show that AMBER obtains gains of up to 1.1 average F1 score on sequence tagging and up to 27.3 average accuracy on retrieval over the XLMR-large model.
  arXiv  Detail & Related papers  (2020-10-15T18:34:13Z)
• CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP [68.2650714613869]
  We propose a data augmentation framework to generate multi-lingual code-switching data to fine-tune mBERT. Compared with the existing work, our method does not rely on bilingual sentences for training, and requires only one training process for multiple target languages.
  arXiv  Detail & Related papers  (2020-06-11T13:15:59Z)

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.