Employing pre-trained language models (LM) to extract contextualized word
representations has achieved state-of-the-art performance on various NLP tasks.
However, applying this technique to noisy transcripts generated by automatic
speech recognizer (ASR) is concerned. Therefore, this paper focuses on making
contextualized representations more ASR-robust. We propose a novel
confusion-aware fine-tuning method to mitigate the impact of ASR errors to
pre-trained LMs. Specifically, we fine-tune LMs to produce similar
representations for acoustically confusable words that are obtained from word
confusion networks (WCNs) produced by ASR. Experiments on the benchmark ATIS
dataset show that the proposed method significantly improves the performance of
spoken language understanding when performing on ASR transcripts. Our source
code is available at this https URL

本研究提出了一种新的混淆感知的微调方法，以减轻 ASR 误差对已预训练的语言模型产生的影响，并在 ATIS 数据集上进行了实验，结果表明该方法显著提高了 ASR 转录文本上口语理解的性能。

针对语音识别鲁棒性的上下文化嵌入在口语理解中的学习

Learning ASR-Robust Contextualized Embeddings for Spoken Language  Understanding

A robust evaluation metric has a profound impact on the development of text
generation systems. A desirable metric compares system output against
references based on their semantics rather than surface forms. In this paper we
investigate strategies to encode system and reference texts to devise a metric
that shows a high correlation with human judgment of text quality. We validate
our new metric, namely MoverScore, on a number of text generation tasks
including summarization, machine translation, image captioning, and
data-to-text generation, where the outputs are produced by a variety of neural
and non-neural systems. Our findings suggest that metrics combining
contextualized representations with a distance measure perform the best. Such
metrics also demonstrate strong generalization capability across tasks. For
ease-of-use we make our metrics available as web service.

本文研究评估文本生成系统的评价指标，提出了基于语义而非形式的指标 MoverScore，并通过多项任务的验证表明采用上下文表示和距离测度相结合的方法可以取得最佳表现，该指标具有较强的泛化能力，并已提供 Web 服务方便使用。

MoverScore: 文本生成的上下文嵌入和地球移动距离评估

MoverScore: Text Generation Evaluating with Contextualized Embeddings  and Earth Mover Distance

Dialog act (DA) recognition is a task that has been widely explored over the
years. Recently, most approaches to the task explored different DNN
architectures to combine the representations of the words in a segment and
generate a segment representation that provides cues for intention. In this
study, we explore means to generate more informative segment representations,
not only by exploring different network architectures, but also by considering
different token representations, not only at the word level, but also at the
character and functional levels. At the word level, in addition to the commonly
used uncontextualized embeddings, we explore the use of contextualized
representations, which provide information concerning word sense and segment
structure. Character-level tokenization is important to capture
intention-related morphological aspects that cannot be captured at the word
level. Finally, the functional level provides an abstraction from words, which
shifts the focus to the structure of the segment. We also explore approaches to
enrich the segment representation with context information from the history of
the dialog, both in terms of the classifications of the surrounding segments
and the turn-taking history. This kind of information has already been proved
important for the disambiguation of DAs in previous studies. Nevertheless, we
are able to capture additional information by considering a summary of the
dialog history and a wider turn-taking context. By combining the best
approaches at each step, we achieve results that surpass the previous
state-of-the-art on generic DA recognition on both SwDA and MRDA, two of the
most widely explored corpora for the task. Furthermore, by considering both
past and future context, simulating annotation scenario, our approach achieves
a performance similar to that of a human annotator on SwDA and surpasses it on
MRDA.

本研究旨在通过考虑不同层级的 tokens 和在历史对话中上下文信息的丰富表达，探索生成更加信息丰富的对话结构代表，我们通过组合最佳的方法，在 SwDA 和 MRDA 上取得了超越以往最佳结果，并在 MRDA 上超越了人类的表现水平。