Collaborative: Discriminative Knowledge-Rich Language Modeling for Machine Translation

该项目研究了一种新方法，用于评估在基于搜索的机器翻译 (MT) 系统中创建的替代翻译假设的流畅性和语法性。 这项任务通常被称为“语言建模”（LM），主要在语音识别的背景下进行探索。然而，当前最先进的语言模型（LM）无法有效区分更流畅的语法翻译和较差的替代方案。 相比之下，所提出的方法“判别知识丰富的语言模型”（DKRLM）明确设计为通过将翻译假设的语言特征与非常大的“干净”单语语料库进行比较来找到搜索空间内最流畅和语法最合规的翻译。直觉是，更多的语法翻译假设应该包含在大型语料库中看到的更高比例的特征。 该项目的一个重要贡献是探索不同类型的语言特征，以确定那些对于比较来说信息最丰富的特征。 此外，还进行判别性训练，将这些特征合并到独立于系统的评分函数中，取代 MT 系统中的传统 LM。 拟议工作的更广泛影响包括更广泛地采用该方法，以及更广泛地使用新的 DKRLM 函数到其他旨在生成流畅语法文本的基于搜索的 NLP 应用程序。 这包括基于搜索的语音识别、自然语言生成 (NLG)、光学字符识别 (OCR)、摘要等方法。

This project investigates a novel approach for assessing the fluency andgrammaticality of alternative translation hypotheses that are created withinsearch-based Machine Translation (MT) systems. This task, commonly termed"Language Modeling" (LM), has been explored primarily in the context of speechrecognition; however, current state-of-the-art language models (LMs) are noteffective at distinguishing between more fluent grammatical translations andtheir poor alternatives. In contrast, the proposed approach, "DiscriminativeKnowledge-Rich Language Modeling" (DKRLM), is explicitly designed to find themost fluent and grammatical translations within the search space by comparingthe linguistic features of the translation hypotheses against very large"clean" monolingual corpora. The intuition is that more grammaticaltranslation hypotheses should contain higher proportions of features seen inthe large corpora. An important contribution of the project is in exploringdifferent types of linguistic features to identify those that are mostinformative for the comparisons. Moreover, discriminative training isperformed to incorporate the features into a system-independent scoringfunction, replacing traditional LMs in MT systems. The broader impacts of theproposed work include both broader adoption for the methodology as well aswider use of the new DKRLM functions to other search-based NLP applicationsthat aim at generating fluent grammatical text. This includes search-basedapproaches to Speech Recognition, Natural Language Generation (NLG), OpticalCharacter Recognition (OCR), Summarization, and others.