Papers by Nikita Moghe
Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking (2021.emnlp-main)
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| Challenge: | Existing methods to make multilingual systems expensive and tedious introduce pipeline of errors. |
| Approach: | They propose to use pre-trained multilingual models to enhance the transfer learning process by intermediate fine-tuning of pretrained multi-lingual models. |
| Outcome: | The proposed approach improves on the cross-lingual dialogue state tracking task with only 10% of the target language task data and zero-shot setup respectively. |
An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT) (2025.acl-long)
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Laurie Burchell, Ona De Gibert Bonet, Nikolay Arefyev, Mikko Aulamo, Marta Bañón, Pinzhen Chen, Mariia Fedorova, Liane Guillou, Barry Haddow, Jan Hajič, Jindřich Helcl, Erik Henriksson, Mateusz Klimaszewski, Ville Komulainen, Andrey Kutuzov, Joona Kytöniemi, Veronika Laippala, Petter Mæhlum, Bhavitvya Malik, Farrokh Mehryary, Vladislav Mikhailov, Nikita Moghe, Amanda Myntti, Dayyán O’Brien, Stephan Oepen, Proyag Pal, Jousia Piha, Sampo Pyysalo, Gema Ramírez-Sánchez, David Samuel, Pavel Stepachev, Jörg Tiedemann, Dušan Variš, Tereza Vojtěchová, Jaume Zaragoza-Bernabeu
| Challenge: | a large number of textual data is needed to train state-of-the-art large language models. |
| Approach: | They propose a collection of monolingual and parallel corpora from the Internet Archive . they document the entire data pipeline and release the code to reproduce it . |
| Outcome: | The proposed collection of monolingual and parallel corpora is based on the HPLT v2 dataset . it includes 8T tokens covering 193 languages and 380M sentence pairs covering 51 languages . |
Multi3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue (2023.findings-acl)
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| Challenge: | Task-oriented dialogue systems are typically constructed for a single domain or language and do not generalise well beyond this. |
| Approach: | They constructed a multilingual, multi-intent, multi domain dataset to support work on Natural Language Understanding (NLU) in ToD across multiple languages and domains simultaneously. |
| Outcome: | The proposed dataset extends the English-only dataset to include manual translations into a range of high, medium, and low resource languages in two domains (banking and hotels). |
Interpreting User Requests in the Context of Natural Language Standing Instructions (2024.findings-naacl)
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| Challenge: | Existing approaches to LLM-based dialogue modeling provide additional context for users to make requests. |
| Approach: | They propose an approach to LLM-based dialogue modeling where persistent user constraints and preferences are provided as additional context for such interfaces. |
| Outcome: | The proposed model achieves a maximum of 46% exact match on the prediction of 2.4K English dialogues with a language-to-program dataset. |
Extrinsic Evaluation of Machine Translation Metrics (2023.acl-long)
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| Challenge: | MT metrics are widely used to distinguish the quality of machine translation systems across relatively large test sets. |
| Approach: | They evaluate the segment-level performance of the most widely used MT metrics by correlating them with how useful they are for downstream tasks. |
| Outcome: | The MT metrics are widely used to distinguish the quality of machine translation systems across relatively large test sets. |
A Dataset for Building Code-Mixed Goal Oriented Conversation Systems (C18-1)
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| Challenge: | Existing data on goal-oriented conversation systems focus on monolingual conversations and there is hardly any work on multilingual and/or code-mixed conversations. |
| Approach: | They build a goal-oriented dialog dataset containing code-mixed conversations using monolingual text from a restaurant reservation dataset. |
| Outcome: | The proposed model is based on a restaurant reservation dataset and will be made publicly available for research purposes. |
Towards Exploiting Background Knowledge for Building Conversation Systems (D18-1)
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| Challenge: | Existing dialog datasets contain a sequence of utterances without any explicit background knowledge associated with them. |
| Approach: | They propose to use movie chats to generate responses by copying unstructured background knowledge . they use a dataset of 9K conversations to test whether responses are generated by copy-and-modify models . |
| Outcome: | The proposed model mimics human process of conversing by copying and/or modifying sentences from unstructured background knowledge. |