MMQA: A Multi-domain Multi-lingual Question-Answering Framework for English and Hindi (L18-1)
Copied to clipboard
| Challenge: | Existing work on multi-domain, multi-lingual question answering is limited to the same language. |
| Approach: | They curate 500 articles in six different domains from the web and create question-answer pairs . they develop a deep learning based model for classifying an input question into coarse and finer categories . |
| Outcome: | The proposed model accuracies 90.12% and 80.30% for coarse and finer classes . the proposed model is the first attempt to create multi-domain, multi-lingual question answering evaluation involving English and Hindi. |
Similar Papers
M2QA: Multi-domain Multilingual Question Answering (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Language varies along several axes, most importantly, language instance and domain . lack of evaluation datasets prevents transfer of NLP systems to non-dominant languages . |
| Approach: | They propose a multi-domain multilingual question answering benchmark to explore cross-lingual cross-domain performance of fine-tuned models and state-of-the-art LLMs. |
| Outcome: | The proposed benchmark compared 13,500 SQuAD 2.0-style question-answer instances in German, Turkish, and Chinese for the domains of product reviews, news, and creative writing. |
MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering (2021.tacl-1)
Copied to clipboard
| 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. |
| Outcome: | The proposed model is based on a multilingual knowledge questions and answers evaluation set with 26 languages. |
Multi-Domain Multilingual Question Answering (2021.emnlp-tutorials)
Copied to clipboard
| Challenge: | Question answering (QA) is one of the most challenging tasks in natural language processing. |
| Approach: | a tutorial examines the state-of-the-art approaches to multi-domain and multilingual QA . they introduce standard benchmarks and discuss out-of the-box training with open-domain QA systems . |
| Outcome: | This tutorial aims to bridge the gap between open-domain and multilingual QA. |
A Unified Framework for Multilingual and Code-Mixed Visual Question Answering (2020.aacl-main)
Copied to clipboard
| Challenge: | Existing techniques for visual question answering focus on English questions, but many applications require a multilingual module. |
| Approach: | They propose a deep learning framework for multilingual and code- mixed visual question answering . they create Hindi and Code-mixed VQA datasets by exploiting linguistic properties of these languages . |
| Outcome: | The proposed model is capable of predicting answers from the questions in Hindi, English or Code- mixed (Hindi-English) languages. |
MLQA: Evaluating Cross-lingual Extractive Question Answering (2020.acl-main)
Copied to clipboard
| Challenge: | Question answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets. |
| Approach: | They present a multi-way aligned extractive QA evaluation benchmark in 7 languages . they evaluate state-of-the-art cross-lingual models and machine-translation-based baselines . |
| Outcome: | The proposed model is based on MLQA, which has over 12K instances in english and 5K in each other language. |
A Multi-Domain Framework for Textual Similarity. A Case Study on Question-to-Question and Question-Answering Similarity Tasks (L18-1)
Copied to clipboard
| Challenge: | Community Question Answering websites are becoming popular and useful source of information for users. |
| Approach: | They propose to use community question answering forum to detect similar questions . they use question-answering similarity task to provide correct answers . |
| Outcome: | The proposed framework provides the first framework on the evaluation of similar questions and question-answering detection on a multi-domain corpora. |
XQA: A Cross-lingual Open-domain Question Answering Dataset (P19-1)
Copied to clipboard
| Challenge: | Open-domain question answering aims to answer questions through text retrieval and reading comprehension . but, the success of these models relies on a massive volume of training data, which is not available in other languages . a new dataset aims at investigating cross-lingual OpenQA . |
| Approach: | They propose to use a dataset for cross-lingual OpenQA research to test models . they use XQA dataset to train models with large volumes of labeled data . |
| Outcome: | The proposed model achieves best results in almost all target languages while the performance is lower than that of English. |
Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation (2022.aacl-main)
Copied to clipboard
| Challenge: | Open-Domain Generative Question Answering has achieved impressive performance in English . combining document-level retrieval with answer generation can generate complete sentences . |
| Approach: | They propose an open-domain approach that combines document retrieval with answer generation to generate complete sentences in English . they propose a cross-lingual generative model that exploits passages written in multiple languages . |
| Outcome: | The proposed model outperforms answer sentence selection baselines for all 5 languages and monolingual pipelines for three out of five languages. |
Towards more equitable question answering systems: How much more data do you need? (2021.acl-short)
Copied to clipboard
| Challenge: | Question answering datasets in English are relatively new, but lack of linguistic diversity in the field is a challenge. |
| Approach: | They propose to use translation and cross-lingual transfer to produce QA systems in multiple languages to improve their performance. |
| Outcome: | The proposed approaches take advantage of existing resources to produce QA systems in multiple languages. |
M3TQA: Massively Multilingual Multitask Table Question Answering (2026.findings-acl)
Copied to clipboard
Daixin Shu, Jian Yang, Zhenhe Wu, Xianjie Wu, Xianfu Cheng, Guan Xiangyuan, Yanghai Wang, Pengfei Wu, Tingyang Yang, Hualei Zhu, Wei Zhang, Ge Zhang, Jiaheng Liu, Zhoujun Li
| Challenge: | Existing multilingual table benchmarks suffer from geolinguistic imbalance - overrepresenting certain languages and lacking sufficient scale for rigorous cross-lingual analysis. |
| Approach: | They propose a framework for massively multilingual table question answering that includes tables expanded to 97 languages from Chinese and English sources. |
| Outcome: | Experiments on state-of-the-art LLMs show that synthetically generated training data significantly boosts performance, especially for low-resource languages. |