Cross-lingual Data Augmentation for Document-grounded Dialog Systems in Low Resource Languages

• URL: http://arxiv.org/abs/2305.14949v2
• Date: Wed, 20 Sep 2023 07:39:19 GMT
• Title: Cross-lingual Data Augmentation for Document-grounded Dialog Systems in Low Resource Languages
• Authors: Qi Gou, Zehua Xia, Wenzhe Du
• Abstract summary: We present a novel pipeline CLEM (Cross-Lingual Enhanced Model) including adversarial training retrieval (Retriever and Re-ranker) and Fid (fusion-in-decoder) generator. To further leverage high-resource language, we also propose an innovative architecture to conduct alignment across different languages with translated training.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a framework to address the issue of data scarcity in Document-Grounded Dialogue Systems(DGDS). Our model leverages high-resource languages to enhance the capability of dialogue generation in low-resource languages. Specifically, We present a novel pipeline CLEM (Cross-Lingual Enhanced Model) including adversarial training retrieval (Retriever and Re-ranker), and Fid (fusion-in-decoder) generator. To further leverage high-resource language, we also propose an innovative architecture to conduct alignment across different languages with translated training. Extensive experiment results demonstrate the effectiveness of our model and we achieved 4th place in the DialDoc 2023 Competition. Therefore, CLEM can serve as a solution to resource scarcity in DGDS and provide useful guidance for multi-lingual alignment tasks.

Related papers

• Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data Selection [49.27067541740956]
  Speech Emotion Recognition (SER) is a crucial component in developing general-purpose AI agents capable of natural human-computer interaction. Building robust multilingual SER systems remains challenging due to the scarcity of labeled data in languages other than English and Chinese. We propose an approach to enhance SER performance in low SER resource languages by leveraging data from high-resource languages.
  arXiv  Detail & Related papers  (2024-09-17T08:36:45Z)
• MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting [53.77590764277568]
  We introduce a novel MoE-CT architecture that separates the base model's learning from the multilingual expansion process. Our design freezes the original LLM parameters, thus safeguarding its performance in high-resource languages, while an appended MoE module, trained on diverse language datasets, augments low-resource language proficiency.
  arXiv  Detail & Related papers  (2024-06-25T11:03:45Z)
• Transferring BERT Capabilities from High-Resource to Low-Resource Languages Using Vocabulary Matching [1.746529892290768]
  This work presents a novel approach to transfer BERT capabilities from high-resource to low-resource languages using vocabulary matching. We conduct experiments on the Silesian and Kashubian languages and demonstrate the effectiveness of our approach to improve the performance of BERT models even when the target language has minimal training data.
  arXiv  Detail & Related papers  (2024-02-22T09:49:26Z)
• Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages [60.162717568496355]
  Large language models (LLMs) have been pre-trained on multilingual corpora. Their performance still lags behind in most languages compared to a few resource-rich languages.
  arXiv  Detail & Related papers  (2024-02-19T15:07:32Z)
• DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models [76.79929883963275]
  DIALIGHT is a toolkit for developing and evaluating multilingual Task-Oriented Dialogue (ToD) systems. It features a secure, user-friendly web interface for fine-grained human evaluation at both local utterance level and global dialogue level. Our evaluations reveal that while PLM fine-tuning leads to higher accuracy and coherence, LLM-based systems excel in producing diverse and likeable responses.
  arXiv  Detail & Related papers  (2024-01-04T11:27:48Z)
• Soft Prompt Decoding for Multilingual Dense Retrieval [30.766917713997355]
  We show that applying state-of-the-art approaches developed for cross-lingual information retrieval to MLIR tasks leads to sub-optimal performance. This is due to the heterogeneous and imbalanced nature of multilingual collections. We present KD-SPD, a novel soft prompt decoding approach for MLIR that implicitly "translates" the representation of documents in different languages into the same embedding space.
  arXiv  Detail & Related papers  (2023-05-15T21:17:17Z)
• Learning Translation Quality Evaluation on Low Resource Languages from Large Language Models [4.168157981135698]
  We show how knowledge can be distilled from Large Language Models (LLMs) to improve upon learned metrics without requiring human annotators. We show that the performance of a BLEURT-like model on lower resource languages can be improved in this way.
  arXiv  Detail & Related papers  (2023-02-07T14:35:35Z)
• A Survey of Multilingual Models for Automatic Speech Recognition [6.657361001202456]
  Cross-lingual transfer is an attractive solution to the problem of multilingual Automatic Speech Recognition. Recent advances in Self Supervised Learning are opening up avenues for unlabeled speech data to be used in multilingual ASR models. We present best practices for building multilingual models from research across diverse languages and techniques.
  arXiv  Detail & Related papers  (2022-02-25T09:31:40Z)
• Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition [54.92161571089808]
  Cross-lingual NER transfers knowledge from rich-resource language to languages with low resources. Existing cross-lingual NER methods do not make good use of rich unlabeled data in target languages. We develop a novel approach based on the ideas of semi-supervised learning and reinforcement learning.
  arXiv  Detail & Related papers  (2021-06-01T05:46:22Z)
• An Empirical Study of Cross-Lingual Transferability in Generative Dialogue State Tracker [33.2309643963072]
  We study the transferability of a cross-lingual generative dialogue state tracking system using a multilingual pre-trained seq2seq model. We also find out the low cross-lingual transferability of our approaches and provides investigation and discussion.
  arXiv  Detail & Related papers  (2021-01-27T12:45:55Z)
• UNKs Everywhere: Adapting Multilingual Language Models to New Scripts [103.79021395138423]
  Massively multilingual language models such as multilingual BERT (mBERT) and XLM-R offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. Due to their limited capacity and large differences in pretraining data, there is a profound performance gap between resource-rich and resource-poor target languages. We propose novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts.
  arXiv  Detail & Related papers  (2020-12-31T11:37:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.