| Challenge: | Existing approaches to identify complex semantic structures are difficult to train from under-annotated sources. |
| Approach: | They exploit relation- and event-relevant language-universal features to train relation or event extractors from source annotations and apply them to target languages. |
| Outcome: | The proposed approach achieves comparable performance to state-of-the-art models trained on 3,000 manually annotated mentions. |
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| Challenge: | Existing approaches for information extraction only use name tagging . Currently, most successful cross-lingual transfer learning methods are limited to sequence labeling . |
| Approach: | They propose a share-and-transfer framework to transfer graph structures across languages . they propose to convert sentences in any language to language-universal graph structures . |
| Outcome: | The proposed framework performs comparable to state-of-the-art models on three languages without annotations. |
Crosslingual Transfer Learning for Relation and Event Extraction via Word Category and Class Alignments (2021.emnlp-main)
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| Challenge: | Existing approaches to crosslingual Relation and Event Extraction (REE) suffer from monolingual bias due to training of models on source language data. |
| Approach: | They propose to use unlabeled data in target language to aid alignment of crosslingual representations by fooling a language discriminator. |
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Multilingual Entity, Relation, Event and Human Value Extraction (N19-4)
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| Challenge: | Existing systems that extract knowledge elements from multiple languages and documents do not aggregate knowledge from multiple documents and languages. |
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Multi-lingual Entity Discovery and Linking (P18-5)
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| Challenge: | This tutorial reviews the framework of cross-lingual EL and motivates it as a broad paradigm for the Information Extraction task. |
| Approach: | This tutorial will review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. |
| Outcome: | The aim of this tutorial is to review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. |
Retrieving Relevant Context to Align Representations for Cross-lingual Event Detection (2023.findings-acl)
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| Challenge: | Existing approaches to cross-lingual transfer learning for event detection are mixed with event-discriminative context. |
| Approach: | They propose a method where representations are augmented with additional context to bridge the gap between languages while enriching contextual information to facilitate ED. |
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Multilingual SubEvent Relation Extraction: A Novel Dataset and Structure Induction Method (2022.findings-emnlp)
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| Challenge: | Existing methods for subevent relation extraction (SRE) focus on sequential order of words in texts to enhance representation learning. |
| Approach: | They propose a method that learns to induce effective graph structures for input texts . they use word alignment frameworks with dependency paths and optimal transport . |
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Bridging the Gap between Native Text and Translated Text through Adversarial Learning: A Case Study on Cross-Lingual Event Extraction (2023.findings-eacl)
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| Challenge: | Recent research in cross-lingual learning has found that combining large-scale pretrained multilingual language models with machine translation can yield good performance. |
| Approach: | They propose a model architecture that jointly encodes a source language input sentence with its translation to the target language during training and takes a target language sentence with it as input during evaluation. |
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Language Embeddings for Typology and Cross-lingual Transfer Learning (2021.acl-long)
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| Challenge: | Recent efforts to leverage multilingual datasets highlight potential of multilingual models that can perform well across various languages. |
| Approach: | They propose to generate language representations that capture relationships among languages and evaluate them using WALS and two extrinsic tasks. |
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Semantic Frame Extraction in Multilingual Olfactory Events (2024.lrec-main)
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| Challenge: | Despite the interest in studying this domain, little effort has been devoted to develop tools and models that can extract olfactory information from large amounts of text in a structured and scalable way. |
| Approach: | They propose a system for multilingual olfactory information extraction covering six European languages, namely English, French, Italian, Dutch, German and Slovene. |
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Project-then-Transfer: Effective Two-stage Cross-lingual Transfer for Semantic Dependency Parsing (2021.eacl-main)
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| Challenge: | Several remarkable contributions have been made in syntactic dependency parsing, especially on universal dependencies. |
| Approach: | They propose to capture cross-linguality by combing annotation projection and model transfer of pre-trained language models. |
| Outcome: | The proposed model parser almost achieved the approximated upper bound. |