RI: Medium: Collaborative Research: Semi-Supervised Discriminative Training of Language Models

该项目正在进行统计语言建模的基础研究，以提高人类语言技术，包括自动语音识别(ASR)和机器翻译(MT)。语言模型(LM)通常是优化的，使用目标语言的文本，为格式良好的句子赋予高概率。这种方法有一个根本的缺陷：优化没有明确地针对完成手头任务所需的各种区别，例如区分(对于ASR)在听觉上容易混淆的不同单词或者(对于MT)表达多义源语言单词的不同目标语言单词。为了克服这一缺点，对LM的判别优化需要大量成对的输入输出序列：用于ASR或源语言(例如汉语)句子的语音及其参考转录以及它们到目标语言(例如，英语)的翻译用于MT。这种资源很昂贵，而且限制了歧视性训练方法的有效性。这个项目与常规截然不同，使用容易获得的、未配对的输入和输出序列来研究歧视性训练：未转录的语音或单语源语言文本和未配对的目标语言文本。正在追求两个关键思想：(I)处理未标记的输入序列(例如语音或中文文本)以学习ASR或MT系统可能遇到的混淆；(Ii)利用未配对的输出序列(英文文本)来区分这些格式良好的句子和系统可能潜在地混淆它们的(假定的)格式错误的句子。这种自我监督的区别训练如果成功，将在影响许多其他应用的基本方式上促进机器智能。

This project is conducting fundamental research in statistical language modeling to improve human language technologies, including automatic speech recognition (ASR) and machine translation (MT).A language model (LM) is conventionally optimized, using text in the target language, to assign high probability to well-formed sentences. This method has a fundamental shortcoming: the optimization does not explicitly target the kinds of distinctions necessary to accomplish the task at hand, such as discriminating (for ASR) between different words that are acoustically confusable or (for MT) between different target-language words that express the multiple meanings of a polysemous source-language word.Discriminative optimization of the LM, which would overcome this shortcoming, requires large quantities of paired input-output sequences: speech and its reference transcription for ASR or source-language (e.g. Chinese) sentences and their translations into the target language (say, English) for MT. Such resources are expensive, and limit the efficacy of discriminative training methods.In a radical departure from convention, this project is investigating discriminative training using easily available, *unpaired* input and output sequences: un-transcribed speech or monolingual source-language text and unpaired target-language text. Two key ideas are being pursued: (i) unlabeled input sequences (e.g. speech or Chinese text) are processed to learn likely confusions encountered by the ASR or MT system; (ii) unpaired output sequences (English text) are leveraged to discriminate between these well-formed sentences from the (supposed) ill-formed sentences the system could potentially confuse them with.This self-supervised discriminative training, if successful, will advance machine intelligence in fundamental ways that impact many other applications.