Learning Domain Specific Language Models for Automatic Speech Recognition through Machine Translation

• URL: http://arxiv.org/abs/2110.10261v1
• Date: Tue, 21 Sep 2021 10:29:20 GMT
• Title: Learning Domain Specific Language Models for Automatic Speech Recognition through Machine Translation
• Authors: Saurav Jha
• Abstract summary: We use Neural Machine Translation as an intermediate step to first obtain translations of task-specific text data. We develop a procedure to derive word confusion networks from NMT beam search graphs. We demonstrate that NMT confusion networks can help to reduce the perplexity of both n-gram and recurrent neural network LMs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic Speech Recognition (ASR) systems have been gaining popularity in the recent years for their widespread usage in smart phones and speakers. Building ASR systems for task-specific scenarios is subject to the availability of utterances that adhere to the style of the task as well as the language in question. In our work, we target such a scenario wherein task-specific text data is available in a language that is different from the target language in which an ASR Language Model (LM) is expected. We use Neural Machine Translation (NMT) as an intermediate step to first obtain translations of the task-specific text data. We then train LMs on the 1-best and N-best translations and study ways to improve on such a baseline LM. We develop a procedure to derive word confusion networks from NMT beam search graphs and evaluate LMs trained on these confusion networks. With experiments on the WMT20 chat translation task dataset, we demonstrate that NMT confusion networks can help to reduce the perplexity of both n-gram and recurrent neural network LMs compared to those trained only on N-best translations.

Related papers

• LANDeRMT: Detecting and Routing Language-Aware Neurons for Selectively Finetuning LLMs to Machine Translation [43.26446958873554]
  Large language models (LLMs) have shown promising results in multilingual translation even with limited bilingual supervision. Recent advancements in large language models (LLMs) have shown promising results in multilingual translation even with limited bilingual supervision. LandeRMT is a framework that selectively finetunes LLMs to textbfMachine textbfTranslation with diverse translation training data.
  arXiv  Detail & Related papers  (2024-09-29T02:39:42Z)
• Towards Zero-Shot Multimodal Machine Translation [64.9141931372384]
  We propose a method to bypass the need for fully supervised data to train multimodal machine translation systems. Our method, called ZeroMMT, consists in adapting a strong text-only machine translation (MT) model by training it on a mixture of two objectives. To prove that our method generalizes to languages with no fully supervised training data available, we extend the CoMMuTE evaluation dataset to three new languages: Arabic, Russian and Chinese.
  arXiv  Detail & Related papers  (2024-07-18T15:20:31Z)
• Beyond MLE: Investigating SEARNN for Low-Resourced Neural Machine Translation [0.09459165957946088]
  This project explored the potential of SEARNN to improve machine translation for low-resourced African languages. Experiments conducted on translation for English to Igbo, French to ewe, and French to ghomala directions. We proved that SEARNN is indeed a viable algorithm to effectively train RNNs on machine translation for low-resourced languages.
  arXiv  Detail & Related papers  (2024-05-20T06:28:43Z)
• Translation-Enhanced Multilingual Text-to-Image Generation [61.41730893884428]
  Research on text-to-image generation (TTI) still predominantly focuses on the English language. In this work, we thus investigate multilingual TTI and the current potential of neural machine translation (NMT) to bootstrap mTTI systems. We propose Ensemble Adapter (EnsAd), a novel parameter-efficient approach that learns to weigh and consolidate the multilingual text knowledge within the mTTI framework.
  arXiv  Detail & Related papers  (2023-05-30T17:03:52Z)
• Active Learning for Neural Machine Translation [0.0]
  We incorporated a technique known Active Learning with the NMT toolkit Joey NMT to reach sufficient accuracy and robust predictions of low-resource language translation. This work uses transformer-based NMT systems; baseline model (BM), fully trained model (FTM), active learning least confidence based model (ALLCM) and active learning margin sampling based model (ALMSM) when translating English to Hindi.
  arXiv  Detail & Related papers  (2022-12-30T17:04:01Z)
• Phrase-level Active Learning for Neural Machine Translation [107.28450614074002]
  We propose an active learning setting where we can spend a given budget on translating in-domain data. We select both full sentences and individual phrases from unlabelled data in the new domain for routing to human translators. In a German-English translation task, our active learning approach achieves consistent improvements over uncertainty-based sentence selection methods.
  arXiv  Detail & Related papers  (2021-06-21T19:20:42Z)
• FDMT: A Benchmark Dataset for Fine-grained Domain Adaptation in Machine Translation [53.87731008029645]
  We present a real-world fine-grained domain adaptation task in machine translation (FDMT) The FDMT dataset consists of four sub-domains of information technology: autonomous vehicles, AI education, real-time networks and smart phone. We make quantitative experiments and deep analyses in this new setting, which benchmarks the fine-grained domain adaptation task.
  arXiv  Detail & Related papers  (2020-12-31T17:15:09Z)
• SJTU-NICT's Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task [111.91077204077817]
  We participated in four translation directions of three language pairs: English-Chinese, English-Polish, and German-Upper Sorbian. Based on different conditions of language pairs, we have experimented with diverse neural machine translation (NMT) techniques. In our submissions, the primary systems won the first place on English to Chinese, Polish to English, and German to Upper Sorbian translation directions.
  arXiv  Detail & Related papers  (2020-10-11T00:40:05Z)
• Multi-task Learning for Multilingual Neural Machine Translation [32.81785430242313]
  We propose a multi-task learning framework that jointly trains the model with the translation task on bitext data and two denoising tasks on the monolingual data. We show that the proposed approach can effectively improve the translation quality for both high-resource and low-resource languages.
  arXiv  Detail & Related papers  (2020-10-06T06:54:12Z)
• Neural Machine Translation: Challenges, Progress and Future [62.75523637241876]
  Machine translation (MT) is a technique that leverages computers to translate human languages automatically. neural machine translation (NMT) models direct mapping between source and target languages with deep neural networks. This article makes a review of NMT framework, discusses the challenges in NMT and introduces some exciting recent progresses.
  arXiv  Detail & Related papers  (2020-04-13T07:53:57Z)

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.