In this paper, we provide a bilingual parallel human-to-human recommendation
dialog dataset (DuRecDial 2.0) to enable researchers to explore a challenging
task of multilingual and cross-lingual conversational recommendation. The
difference between DuRecDial 2.0 and existing conversational recommendation
datasets is that the data item (Profile, Goal, Knowledge, Context, Response) in
DuRecDial 2.0 is annotated in two languages, both English and Chinese, while
other datasets are built with the setting of a single language. We collect 8.2k
dialogs aligned across English and Chinese languages (16.5k dialogs and 255k
utterances in total) that are annotated by crowdsourced workers with strict
quality control procedure. We then build monolingual, multilingual, and
cross-lingual conversational recommendation baselines on DuRecDial 2.0.
Experiment results show that the use of additional English data can bring
performance improvement for Chinese conversational recommendation, indicating
the benefits of DuRecDial 2.0. Finally, this dataset provides a challenging
testbed for future studies of monolingual, multilingual, and cross-lingual
conversational recommendation.

提供了一种双语平行人对人推荐对话数据集并通过采用严格的质量控制程序进行了注释， 实验结果表明，使用额外的英文数据对汉语对话推荐有性能提升作用。

DuRecDial 2.0：一份用于对话式推荐的双语平行语料库

DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational  Recommendation

In Transformer-based neural machine translation (NMT), the positional
encoding mechanism helps the self-attention networks to learn the source
representation with order dependency, which makes the Transformer-based NMT
achieve state-of-the-art results for various translation tasks. However,
Transformer-based NMT only adds representations of positions sequentially to
word vectors in the input sentence and does not explicitly consider reordering
information in this sentence. In this paper, we first empirically investigate
the relationship between source reordering information and translation
performance. The empirical findings show that the source input with the target
order learned from the bilingual parallel dataset can substantially improve
translation performance. Thus, we propose a novel reordering method to
explicitly model this reordering information for the Transformer-based NMT. The
empirical results on the WMT14 English-to-German, WAT ASPEC
Japanese-to-English, and WMT17 Chinese-to-English translation tasks show the
effectiveness of the proposed approach.

本文介绍了如何使用双语平行数据集来模拟目标语序，在改进了位置编码机制的基础上，提出了一种新的序列重排序方法来显式地模拟源语句子的重排序信息，同时实验证明，该方法在多种翻译任务中均有效。

神经机器翻译的显式重排序

Explicit Reordering for Neural Machine Translation

This paper presents BiPaR, a bilingual parallel novel-style machine reading
comprehension (MRC) dataset, developed to support multilingual and
cross-lingual reading comprehension. The biggest difference between BiPaR and
existing reading comprehension datasets is that each triple (Passage, Question,
Answer) in BiPaR is written parallelly in two languages. We collect 3,667
bilingual parallel paragraphs from Chinese and English novels, from which we
construct 14,668 parallel question-answer pairs via crowdsourced workers
following a strict quality control procedure. We analyze BiPaR in depth and
find that BiPaR offers good diversification in prefixes of questions, answer
types and relationships between questions and passages. We also observe that
answering questions of novels requires reading comprehension skills of
coreference resolution, multi-sentence reasoning, and understanding of implicit
causality, etc. With BiPaR, we build monolingual, multilingual, and
cross-lingual MRC baseline models. Even for the relatively simple monolingual
MRC on this dataset, experiments show that a strong BERT baseline is over 30
points behind human in terms of both EM and F1 score, indicating that BiPaR
provides a challenging testbed for monolingual, multilingual and cross-lingual
MRC on novels. The dataset is available at this https URL

本研究提出了 BiPaR—— 一种双语平行小说式机器阅读理解 (MRC) 数据集，它是为支持多语言和跨语言阅读理解而开发的。BiPaR 具有好的问题前缀、答案类型多样性和问题与段落之间的关系多样性，需要核心指代消解、多句子推理和隐含因果关系等阅读理解技能。实验证明，强大的 BERT 模型在这个数据集上与人类之间的差距非常大，BiPaR 为小说的单语、多语和跨语言 MRC 提供了一个具有挑战性的试验平台。