SGER: Self-Supervised Discriminative Training of Statistical Language Models

这一小笔探索性研究基金正在研究用于各种人类语言技术的统计语言模型的鉴别训练的新方法，如自动语音识别(ASR)和机器翻译(MT)。语言模型(LM)通常是通过正则化的最大似然法从目标领域的大量文本语料库中估计出来的。鉴别标准在ASR中得到了一些成功的应用，但由于需要额外的转录语音语料库来区分正确的单词序列和不正确的队列，这些标准的巨大前景受到了限制。这个项目正在探索在不需要大量人工标注的情况下区分估计语言模型的方法，即ASR的转录语音或MT的平行文本。正在探索的关键思想是，如果有大量(比方说)单语言中文文本，那么通过尝试使用现有的机器翻译系统将该文本翻译成(比方说)英语并检查哪些英语单词和短语最频繁地相互竞争，可以准确地估计汉语单词和短语的机器翻译队列。在任何特定的情况下，没有必要知道队列集合中的哪些相互竞争的单词或短语是正确的翻译！只需了解谁最常参加竞争就够了。研究人员正在使用单一语言的英语文本来探索区分队列集合中每个成员及其假定竞争对手的观察到的发病率的特征；从而综合得出区分训练的数据。他们正在调查这种有区别的训练是否专门针对机器翻译系统所面临的最令人衰弱的歧义。这个项目通过探索统计语言模型，使ASR和MT研究社区都受益，这些统计语言模型可以在不需要人工干预的情况下适应不断变化的任务或语言使用，并且对人工标注数据的依赖程度较低。ASR和机器翻译的进步反过来又将促进以多种语文和媒体更有效地利用计算机辅助获取信息。

Title: Self-Supervised Discriminative Training of Statistical Language ModelsThis Small Grant for Exploratory Research is investigating novel methods for discriminative training of statistical language models for application to various human language technologies, such as automatic speech recognition (ASR) and machine translation (MT).A language model (LM) is conventionally estimated from a large corpus of text in the target domain via regularized maximum likelihood. Discriminative criteria have been used with some success in ASR, but their immense promise has been curtailed by the requirement of an additional corpus of transcribed speech needed to discriminate between correct word sequences and their incorrect ?cohorts.? This project is exploring ways to discriminatively estimate language models without requiring massive manual annotation, namely, transcribed speech for ASR or parallel text for MT.The key idea being explored is that if a large amount of (say) monolingual Chinese text is available, then the MT cohorts of Chinese words and phrases may be accurately estimated by attempting to translate this text into (say) English using an existing MT system and examining which English words and phrases are most frequently in competition with each other. It is not necessary to know which of the competing words or phrases in a cohort set is the correct translation in any particular instance! It suffices to learn who are most often in competition. The investigators are using monolingual English text to explore features that discriminate between observed incidences of each member of a cohort set and its putative competitors; the data for discriminative training are thus derived synthetically. They are investigating if such a discriminatively trained LM specifically targets the most debilitating ambiguities faced by the MT system. The ASR counterpart, with cohort sets derived from automatic transcription of unannotated speech, is also being explored.This project benefits both the ASR and MT research communities by exploring statistical language models that can adapt without human intervention to changing tasks or language-use, and that are less reliant on manually annotated data. Advances in ASR and MT in turn will facilitate more effective computer-aided access to information in multiple languages and media.