A Cross-lingual Messenger with Keyword Searchable Phrases for the Travel Domain (C18-2)
Copied to clipboard
| Challenge: | Query Translator is a cross-lingual messaging app for the travel domain that automatically translates conversations . the application addresses common cross-linguistic communication issues such as translation accuracy, speed, privacy and personalization. |
| Approach: | They present a cross-lingual messaging app that automatically translates conversations while supporting keyword-to-sentence matching. |
| Outcome: | The proposed app translates conversations while supporting keyword-to-sentence matching. |
Similar Papers
CL-QR: Cross-Lingual Enhanced Query Reformulation for Multi-lingual Conversational AI Agents (2023.emnlp-industry)
Copied to clipboard
| Challenge: | Existing QR systems that reformulate defective user queries are limited in English due to the scarcity of non-English QR labels. |
| Approach: | They propose a query reformulation method which reformulates defective user queries to improve non-English QR performance. |
| Outcome: | The proposed framework improves non-English QR performance by leveraging abundant reformulation resources in English. |
Effective QA-Driven Annotation of Predicate–Argument Relations Across Languages (2026.eacl-long)
Copied to clipboard
| Challenge: | Explicit representations of predicate-argument relations are a cornerstone of natural language understanding. |
| Approach: | They propose a cross-linguistic projection approach that reuses an English QA-SRL parser within a constrained translation and word-alignment pipeline to automatically generate question-answer annotations aligned with target-language predicates. |
| Outcome: | The proposed approach outperforms strong multilingual LLMs in Hebrew, Russian, and French. |
XOR QA: Cross-lingual Open-Retrieval Question Answering (2021.naacl-main)
Copied to clipboard
| 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 . |
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. |
Document Translation vs. Query Translation for Cross-Lingual Information Retrieval in the Medical Domain (2020.acl-main)
Copied to clipboard
| Challenge: | Existing studies of document translation and query translation are outdated and do not reflect the current advances in machine translation. |
| Approach: | They compare document translation and query translation approaches to cross-lingual information retrieval . they exploit Statistical Machine Translation and Neural Machine Translation paradigms to translate queries into English and English . |
| Outcome: | The proposed approach outperforms the DT approach in translation quality and retrieval quality. |
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities (2024.emnlp-main)
Copied to clipboard
Ming Zhang, Caishuang Huang, Yilong Wu, Shichun Liu, Huiyuan Zheng, Yurui Dong, Yujiong Shen, Shihan Dou, Jun Zhao, Junjie Ye, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Current datasets cater to user-led systems and are limited to predefined specific scenarios and slots. |
| Approach: | They propose to use a Chinese dialogue dataset to train a model that authentically simulates human-computer dialogues in 30 popular life service scenarios. |
| Outcome: | The proposed model achieves a joint accuracy of 75.09% in out-of-domain evaluations . it also achieves notable abilities in slot filling and questioning . |
Conversing with databases: Practical Natural Language Querying (2023.emnlp-industry)
Copied to clipboard
| Challenge: | Large amount of companies' data is stored in relational databases . quick hypotheses validation is rarely, if ever, possible for majority of nontechnical business stakeholders. |
| Approach: | They propose a hybrid NLQ system for conversational DB querying that allows non-technical users to formulate data requests as natural language questions. |
| Outcome: | The proposed system is based on a hybrid NLQ (Natural Language Querying) system for conversational DB querying. |
SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage (P19-3)
Copied to clipboard
Elizabeth Boschee, Joel Barry, Jayadev Billa, Marjorie Freedman, Thamme Gowda, Constantine Lignos, Chester Palen-Michel, Michael Pust, Banriskhem Kayang Khonglah, Srikanth Madikeri, Jonathan May, Scott Miller
| Challenge: | a new cross-lingual information retrieval system for low-resource languages is available in less-frequently-taught languages . a multilingual system can search for relevant information in a haystack of documents in swahili or Somali . human-driven approaches to this problem are complicated in 'low-resourced' languages aaron sagar: "the key role played by humans in triaging results is complicated" |
| Approach: | They propose an end-to-end cross-lingual information retrieval system for low-resource languages . the system enables English speakers to search foreign language repositories using English queries . it summarizes the retrieved documents in English with respect to a particular information need . |
| Outcome: | The proposed system achieves top performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. |
TS-SQL: Test-driven Self-refinement for Text-to-SQL (2025.findings-emnlp)
Copied to clipboard
| Challenge: | null |
| Approach: | null |
| Outcome: | null |
Cross-lingual Text-to-SQL Semantic Parsing with Representation Mixup (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Experimental results show that Rex can benefit from cross-lingual training and improve the effectiveness of semantic parsers. |
| Approach: | They propose a Representation Mixup Framework for effectively exploiting translations in the cross-lingual Text-to-SQL task. |
| Outcome: | The proposed framework can benefit from cross-lingual training and improve the effectiveness of semantic parsers, achieving state-of-the-art performance. |