Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning

• URL: http://arxiv.org/abs/2403.02893v2
• Date: Fri, 22 Mar 2024 07:44:32 GMT
• Title: Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning
• Authors: Zhitao He, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, Zhiqiang Zhang, Mengshu Sun, Jun Zhao,
• Abstract summary: Event Causality Identification (ECI) refers to the detection of causal relations between events in texts. We propose a Heterogeneous Graph Interaction Model with Multi-granularity Contrastive Transfer Learning (GIMC) for document-level ECI. Our framework outperforms the previous state-of-the-art model by 9.4% and 8.2% of average F1 score on monolingual and multilingual scenarios respectively.
• Score: 22.389718537939174
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Event Causality Identification (ECI) refers to the detection of causal relations between events in texts. However, most existing studies focus on sentence-level ECI with high-resource languages, leaving more challenging document-level ECI (DECI) with low-resource languages under-explored. In this paper, we propose a Heterogeneous Graph Interaction Model with Multi-granularity Contrastive Transfer Learning (GIMC) for zero-shot cross-lingual document-level ECI. Specifically, we introduce a heterogeneous graph interaction network to model the long-distance dependencies between events that are scattered over a document. Then, to improve cross-lingual transferability of causal knowledge learned from the source language, we propose a multi-granularity contrastive transfer learning module to align the causal representations across languages. Extensive experiments show our framework outperforms the previous state-of-the-art model by 9.4% and 8.2% of average F1 score on monolingual and multilingual scenarios respectively. Notably, in the multilingual scenario, our zero-shot framework even exceeds GPT-3.5 with few-shot learning by 24.3% in overall performance.

Related papers

• Adapting Language Models to Indonesian Local Languages: An Empirical Study of Language Transferability on Zero-Shot Settings [1.1556013985948772]
  We evaluate transferability of pre-trained language models to low-resource Indonesian local languages.<n>We group the target languages into three categories: seen, partially seen, and unseen.<n> Multilingual models perform best on seen languages, moderately on partially seen ones, and poorly on unseen languages.<n>We find that MAD-X significantly improves performance, especially for seen and partially seen languages, without requiring labeled data in the target language.
  arXiv  Detail & Related papers  (2025-07-02T12:17:55Z)
• Cross-Lingual Pitfalls: Automatic Probing Cross-Lingual Weakness of Multilingual Large Language Models [55.14276067678253]
  This paper introduces a novel methodology for efficiently identifying inherent cross-lingual weaknesses in Large Language Models (LLMs)<n>We construct a new dataset of over 6,000 bilingual pairs across 16 languages using this methodology, demonstrating its effectiveness in revealing weaknesses even in state-of-the-art models.<n>Further experiments investigate the relationship between linguistic similarity and cross-lingual weaknesses, revealing that linguistically related languages share similar performance patterns.
  arXiv  Detail & Related papers  (2025-05-24T12:31:27Z)
• Cross-Linguistic Transfer in Multilingual NLP: The Role of Language Families and Morphology [0.0]
  Cross-lingual transfer has become a crucial aspect of multilingual NLP.<n>This paper investigates cross-linguistic transfer through the lens of language families and morphology.
  arXiv  Detail & Related papers  (2025-05-20T04:19:34Z)
• Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis [8.770572911942635]
  We introduce novel evaluation datasets in several less-resourced languages. We experiment with a range of approaches including the use of machine translation. We show that language similarity is not in itself sufficient for predicting the success of cross-lingual transfer.
  arXiv  Detail & Related papers  (2024-09-30T07:59:41Z)
• mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning [54.523172171533645]
  Cross-lingual named entity recognition (CrossNER) faces challenges stemming from uneven performance due to the scarcity of multilingual corpora. We propose Multi-view Contrastive Learning for Cross-lingual Named Entity Recognition (mCL-NER) Our experiments on the XTREME benchmark, spanning 40 languages, demonstrate the superiority of mCL-NER over prior data-driven and model-based approaches.
  arXiv  Detail & Related papers  (2023-08-17T16:02:29Z)
• RC3: Regularized Contrastive Cross-lingual Cross-modal Pre-training [84.23022072347821]
  We propose a regularized cross-lingual visio-textual contrastive learning objective that constrains the representation proximity of weakly-aligned visio-textual inputs. Experiments on 5 downstream multi-modal tasks across 6 languages demonstrate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2023-05-13T14:41:05Z)
• Improving the Cross-Lingual Generalisation in Visual Question Answering [40.86774711775718]
  multilingual vision-language pretrained models show poor cross-lingual generalisation when applied to non-English data. In this work, we explore the poor performance of these models on a zero-shot cross-lingual visual question answering (VQA) task. We improve cross-lingual transfer with three strategies: (1) we introduce a linguistic prior objective to augment the cross-entropy loss with a similarity-based loss to guide the model during training, (2) we learn a task-specific subnetwork that improves cross-lingual generalisation and reduces variance without model modification, and (3) we augment training examples using synthetic code
  arXiv  Detail & Related papers  (2022-09-07T08:07:43Z)
• A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference [54.678516076366506]
  Natural Language Inference (NLI) is a growingly essential task in natural language understanding. Here we propose a multi-level supervised contrastive learning framework named MultiSCL for low-resource natural language inference.
  arXiv  Detail & Related papers  (2022-05-31T05:54:18Z)
• IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages [87.5457337866383]
  We introduce the Image-Grounded Language Understanding Evaluation benchmark. IGLUE brings together visual question answering, cross-modal retrieval, grounded reasoning, and grounded entailment tasks across 20 diverse languages. We find that translate-test transfer is superior to zero-shot transfer and that few-shot learning is hard to harness for many tasks.
  arXiv  Detail & Related papers  (2022-01-27T18:53:22Z)
• Improving Low-resource Reading Comprehension via Cross-lingual Transposition Rethinking [0.9236074230806579]
  Extractive Reading (ERC) has made tremendous advances enabled by the availability of large-scale high-quality ERC training data. Despite of such rapid progress and widespread application, the datasets in languages other than high-resource languages such as English remain scarce. We propose a Cross-Lingual Transposition ReThinking (XLTT) model by modelling existing high-quality extractive reading comprehension datasets in a multilingual environment.
  arXiv  Detail & Related papers  (2021-07-11T09:35:16Z)
• Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings [41.148892848434585]
  We propose a novel framework to align contextual embeddings at the sense level by leveraging cross-lingual signal from bilingual dictionaries only. We operationalize our framework by first proposing a novel sense-aware cross entropy loss to model word senses explicitly. We then propose a sense alignment objective on top of the sense-aware cross entropy loss for cross-lingual model pretraining, and pretrain cross-lingual models for several language pairs.
  arXiv  Detail & Related papers  (2021-03-11T04:55:35Z)
• GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction [107.8262586956778]
  We introduce graph convolutional networks (GCNs) with universal dependency parses to learn language-agnostic sentence representations. GCNs struggle to model words with long-range dependencies or are not directly connected in the dependency tree. We propose to utilize the self-attention mechanism to learn the dependencies between words with different syntactic distances.
  arXiv  Detail & Related papers  (2020-10-06T20:30:35Z)
• Cross-lingual Spoken Language Understanding with Regularized Representation Alignment [71.53159402053392]
  We propose a regularization approach to align word-level and sentence-level representations across languages without any external resource. Experiments on the cross-lingual spoken language understanding task show that our model outperforms current state-of-the-art methods in both few-shot and zero-shot scenarios.
  arXiv  Detail & Related papers  (2020-09-30T08:56:53Z)

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.