| Challenge: | Existing work on questions has focused on understanding the structure of questions per se . a few approaches explicitly focus on information-seeking questions, but this work is either based on big data or crowdsourcing. |
| Approach: | They propose a dependency-parsed, parallel multilingual corpus of information-seeking and non-information-seeing questions . they employ a linguistically motivated rule-based system that uses linguistic cues from one language to help classify questions across other languages. |
| Outcome: | The proposed system correctly classifies questions in 79% of cases, compared to other systems. |
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| Challenge: | a dataset of 40k information-seeking questions across seven languages is used to answer multilingual question answering tasks. |
| Approach: | They propose a task framework that allows questions from one language to be answered via answer content from another language. |
| Outcome: | The proposed framework can be used to answer questions from one language to another . the dataset was built on 40K questions across 7 languages, but could not find same-language answers . |
A Corpus for Multilingual Document Classification in Eight Languages (L18-1)
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| Challenge: | a subset of the Reuters corpus volume 2 is used to evaluate cross-lingual document classification . current best practice is to evaluate document classification on resources in one language and transfer it to another without additional resources. |
| Approach: | They propose to use a subset of the Reuters corpus to evaluate cross-lingual document classification . they propose to add Italian, Russian, Japanese and Chinese to the subset . |
| Outcome: | The proposed subset of the Reuters corpus has balanced class priors for eight languages. |
A System for Answering Simple Questions in Multiple Languages (2023.acl-demo)
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| Challenge: | Existing knowledge graph question answering systems are limited to simple questions, but they can be used to answer complex questions. |
| Approach: | They propose a multilingual Knowledge Graph Question Answering technique that orders potential responses based on the distance between the question’s text embeddings and the answer’s graph embedds. |
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MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering (2021.tacl-1)
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| Challenge: | Existing multilingual QA datasets lack linguistic diversity and comparable evaluation between languages. |
| Approach: | They propose a multilingual question-answer evaluation set with 10k English queries and human translations of them into 25 additional languages and dialects. |
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TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages (2020.tacl-1)
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Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, Jennimaria Palomaki
| Challenge: | Existing models for multilingual modeling are based on a set of typological features that are used to express meaning in languages such as English. |
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What Are the Implications of Your Question? Non-Information Seeking Question-Type Identification in CNN Transcripts (2024.lrec-main)
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| Challenge: | Non-information seeking questions capture subtle dynamics of human discourse . authors use dataset of over 1,500 information-seeking questions and NISQs as benchmark . |
| Approach: | They use a dataset of over 1,500 information-seeking question(ISQ) and NISQ to evaluate human and machine performance on classifying fine-grained NISq types. |
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A Practical Toolkit for Multilingual Question and Answer Generation (2023.acl-demo)
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| Challenge: | Generating questions and answers from text is a challenging task due to the expected structured output. |
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WikiBank: Using Wikidata to Improve Multilingual Frame-Semantic Parsing (2020.lrec-1)
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| Challenge: | Frame-semantic annotations exist for a tiny fraction of the world’s languages, however, Wikidata provides a common, distant supervision signal for semantic parsers. |
| Approach: | They propose a multilingual resource with partial semantic dependency structures that can be used to extend pre-existing resources rather than creating new man-made resources from scratch. |
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Cross-Lingual Training for Automatic Question Generation (P19-1)
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| Challenge: | Automatic question generation is a challenging problem in natural language understanding . manual curating a dataset of comparable size for a new language is tedious and expensive. |
| Approach: | They propose to reuse available large QG dataset in a secondary language to learn a QG model for a primary language. |
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Cross-lingual Transfer for Automatic Question Generation by Learning Interrogative Structures in Target Languages (2024.emnlp-main)
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| Challenge: | Existing automatic question generation datasets focus on English, resulting in data gaps for other languages. |
| Approach: | They propose a cross-lingual transfer method that allows models to generate questions in low-resource languages. |
| Outcome: | The proposed method outperforms other models and achieves comparable performance across languages. |