| Challenge: | Developing conversational systems that can converse in many languages is an interesting challenge for natural language processing. |
| Approach: | They propose multilingual addressee and response selection task for conversational systems . they use a multilingual conversation dataset to evaluate their methods . |
| Outcome: | The proposed methods can predict addressee and response in multiple languages . they show that the methods work in a multilingual conversation dataset . |
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A Survey of Multilingual Models for Automatic Speech Recognition (2022.lrec-1)
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| Challenge: | Automatic Speech Recognition (ASR) systems have achieved human-like performance for a few languages, but the majority of the world’s languages do not have usable systems due to the lack of large speech datasets to train these models. |
| Approach: | They propose to use unlabeled speech data to build multilingual ASR models that can be used for improved performance on low-resource languages. |
| Outcome: | The proposed models can be used to improve performance on low-resource languages by using unlabeled speech data. |
Towards more equitable question answering systems: How much more data do you need? (2021.acl-short)
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| 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. |
Evaluating Cross-Lingual Transfer Learning Approaches in Multilingual Conversational Agent Models (2020.coling-industry)
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| Challenge: | Existing voice assistant models are developed for each region or language, requiring linear effort to develop and maintain. |
| Approach: | They propose a general multilingual model framework for natural language understanding models . they show multilingual models can reach same or better performance compared to monolingual models a . |
| Outcome: | The proposed model framework can bootstrap new language models faster and reduce effort . it can reach same or better performance compared to monolingual models across language-specific test data . |
Multi-Source Multi-Type Knowledge Exploration and Exploitation for Dialogue Generation (2023.emnlp-main)
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| Challenge: | Existing models focus on identifying specific types of dialogue knowledge and utilizing corresponding datasets for training, but lack generalization capabilities and computational resources. |
| Approach: | They propose a framework that explores multi-source multi-type knowledge from LLMs by leveraging diverse datasets and exploits it for response generation. |
| Outcome: | The proposed framework exploits multi-source multi-type knowledge from LLMs to generate coherent, informative, and fluent responses. |
MEEP: Is this Engaging? Prompting Large Language Models for Dialogue Evaluation in Multilingual Settings (2023.findings-emnlp)
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| Challenge: | Existing metrics for engagingness evaluate the response without the conversation history, are designed for one dataset, or have limited correlation with human annotations. |
| Approach: | They propose to use large language models to evaluate engagingness in dialogue . they propose to include prompts and translated prompts in the model . |
| Outcome: | The proposed model outperforms existing methods on evaluation of engagingness in dialogue across languages. |
Towards Zero-Shot Multilingual Transfer for Code-Switched Responses (2023.acl-long)
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| Challenge: | Recent task-oriented dialog systems have had great success building English-based personal assistants, but extending these systems to a global audience may take tremendous efforts. |
| Approach: | They propose a framework that allows for efficient transfer by learning task-specific representations and encapsulating source and target language representations. |
| Outcome: | The proposed framework is able to successfully transfer language knowledge even when the target language corpus is limited. |
A Survey of Multilingual Reasoning in Language Models (2025.findings-emnlp)
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| Challenge: | This survey provides the first in-depth review of multilingual reasoning in Language Models. |
| Approach: | This survey provides the first in-depth review of multilingual reasoning in LMs. |
| Outcome: | The present study provides the first in-depth review of multilingual reasoning in LMs. |
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)
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| Challenge: | Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment. |
| Approach: | They propose to equip a pre-trained language model with a knowledge selection module to generate knowledge-grounded dialogues. |
| Outcome: | The proposed model outperforms state-of-the-art methods in evaluation and human judgment. |
Natural Language Processing for Multilingual Task-Oriented Dialogue (2022.acl-tutorials)
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| Challenge: | a tutorial will examine the challenges and gaps in multilingual ToD research . multilingual systems are difficult to build, and are limited to English and other languages . |
| Approach: | This tutorial will discuss the importance of multilingual task-oriented dialogue systems . it will provide an overview of current research gaps, challenges and initiatives related to multilingual ToD systems - with a particular focus on their connections to current research and challenges in multilingual and low-resource NLP. |
| Outcome: | This tutorial will provide an overview of current research gaps, challenges and initiatives related to multilingual ToD systems. |
Cross-lingual Transfer Learning with Data Selection for Large-Scale Spoken Language Understanding (D19-1)
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| Challenge: | Existing approaches to improve cross-lingual transfer learning on spoken language are pre-train on all available supervised data from another language. |
| Approach: | They propose a language model based source-language data selection method for cross-lingual transfer learning in spoken language understanding. |
| Outcome: | The proposed method reduces training time and improves model performance on spoken language understanding. |