This study proposes a multitask learning architecture for extractive
summarization with coherence boosting. The architecture contains an extractive
summarizer and coherent discriminator module. The coherent discriminator is
trained online on the sentence vectors of the augmented textual input, thus
improving its general ability of judging whether the input sentences are
coherent. Meanwhile, we maximize the coherent scores from the coherent
discriminator by updating the parameters of the summarizer. To make the
extractive sentences trainable in a differentiable manner, we introduce two
strategies, including pre-trained converting model (model-based) and converting
matrix (MAT-based) that merge sentence representations. Experiments show that
our proposed method significantly improves the proportion of consecutive
sentences in the extracted summaries based on their positions in the original
article (i.e., automatic sentence-level coherence metric), while the goodness
in terms of other automatic metrics (i.e., Rouge scores and BertScores) are
preserved. Human evaluation also evidences the improvement of coherence and
consistency of the extracted summaries given by our method.

提出了一种多任务学习架构，用于基于从文本中提取摘要，其中包含摘要器和连贯判别器模块，通过预训练的转换模型（基于模型）和转换矩阵（MAT-based）将句子表示合并，实现了可微分训练，并以此最大化了连贯判别器的连贯度分数，并通过人工评估证实了我们方法提高了从原始文章位置开始提取的连续句子的比例以及有效保留了其他自动评估指标。

多任务学习增强提取式摘要的连贯性

Enhancing Coherence of Extractive Summarization with Multitask Learning

Due to the unfamiliarity to particular words(or proper nouns) for
ingredients, non-native English speakers can be extremely confused about the
ordering process in restaurants like Subway. Thus, We developed a dialogue
system, which supports Chinese(Mandarin)1 and English2 at the same time. In
other words, users can switch arbitrarily between Chinese(Mandarin) and English
as the conversation is being conducted. This system is specifically designed
for Subway ordering3. In BilDOS, we designed a Discriminator module to tell the
language is being used in inputted user utterance, a Translator module to
translate used language into English if it is not English, and a Dialogue
Manager module to detect the intention within inputted user utterances, handle
outlier inputs by throwing clarification requests, map detected Intention and
detailed Keyword4 into a particular intention class, locate the current
ordering process, continue to give queries to finish the order, conclude the
order details once the order is completed, activate the evaluation process when
the conversation is done.

为了帮助非英语母语者在 Subway 餐厅点餐时解决单词翻译问题，我们开发了一个中英文混合的对话系统，包括翻译模块和对话管理模块等，用于帮助用户完成点餐流程。

Bil-DOS: 双语地铁对话排序系统

Bil-DOS: A Bi-lingual Dialogue Ordering System (for Subway)

Incorporating personal preference is crucial in advanced machine translation
tasks. Despite the recent advancement of machine translation, it remains a
demanding task to properly reflect personal style. In this paper, we introduce
a personalized automatic post-editing framework to address this challenge,
which effectively generates sentences considering distinct personal behaviors.
To build this framework, we first collect post-editing data that connotes the
user preference from a live machine translation system. Specifically,
real-world users enter source sentences for translation and edit the
machine-translated outputs according to the user's preferred style. We then
propose a model that combines a discriminator module and user-specific
parameters on the APE framework. Experimental results show that the proposed
method outperforms other baseline models on four different metrics (i.e., BLEU,
TER, YiSi-1, and human evaluation).

本文介绍一种个性化自动后编辑框架来解决机器翻译中反映个人喜好的挑战，其使用鉴别器模块和用户特定参数的模型，在四种不同指标（BLEU、TER、YiSi-1 和人类评估）上优于基线模型。