Short feedback responses, such as backchannels, play an important role in
spoken dialogue. So far, most of the modeling of feedback responses has focused
on their timing, often neglecting how their lexical and prosodic form influence
their contextual appropriateness and conversational function. In this paper, we
investigate the possibility of embedding short dialogue contexts and feedback
responses in the same representation space using a contrastive learning
objective. In our evaluation, we primarily focus on how such embeddings can be
used as a context-feedback appropriateness metric and thus for feedback
response ranking in U.S. English dialogues. Our results show that the model
outperforms humans given the same ranking task and that the learned embeddings
carry information about the conversational function of feedback responses.

通过对话语境和反馈响应进行对比学习，本文研究了将其嵌入到相同表示空间的可能性。通过使用嵌入模型评估其在美式英语对话中作为上下文 - 反馈适当性度量和反馈响应排序的效果，结果表明该模型在相同排序任务上优于人类，且学习到的嵌入包含了反馈响应的会话功能信息。

口语对话中上下文和反馈嵌入的联合学习

Joint Learning of Context and Feedback Embeddings in Spoken Dialogue

Lexically constrained neural machine translation (LCNMT), which controls the
translation generation with pre-specified constraints, is important in many
practical applications. Current approaches to LCNMT typically assume that the
pre-specified lexical constraints are contextually appropriate. This assumption
limits their application to real-world scenarios where a source lexicon may
have multiple target constraints, and disambiguation is needed to select the
most suitable one. In this paper, we propose disambiguated LCNMT (D-LCNMT) to
solve the problem. D-LCNMT is a robust and effective two-stage framework that
disambiguates the constraints based on contexts at first, then integrates the
disambiguated constraints into LCNMT. Experimental results show that our
approach outperforms strong baselines including existing data augmentation
based approaches on benchmark datasets, and comprehensive experiments in
scenarios where a source lexicon corresponds to multiple target constraints
demonstrate the constraint disambiguation superiority of our approach.

本文介绍了一种基于上下文的约束消岐技术，提出了一种称为 D-LCNMT 的框架，使得预先指定的词汇约束在真实世界的情境中更加合适和精确，实验证明该方法在数据扩充和多个目标约束词汇方面优于先前的方法。

词汇消歧约束的神经机器翻译

Disambiguated Lexically Constrained Neural Machine Translation

We release a new benchmark for lexical substitution, the task of finding
appropriate substitutes for a target word in a context. To assist humans with
writing, lexical substitution systems can suggest words that humans cannot
easily think of. However, existing benchmarks depend on human recall as the
only source of data, and therefore lack coverage of the substitutes that would
be most helpful to humans. Furthermore, annotators often provide substitutes of
low quality, which are not actually appropriate in the given context. We
collect higher-coverage and higher-quality data by framing lexical substitution
as a classification problem, guided by the intuition that it is easier for
humans to judge the appropriateness of candidate substitutes than conjure them
from memory. To this end, we use a context-free thesaurus to produce candidates
and rely on human judgement to determine contextual appropriateness. Compared
to the previous largest benchmark, our Swords benchmark has 4.1x more
substitutes per target word for the same level of quality, and its substitutes
are 1.5x more appropriate (based on human judgement) for the same number of
substitutes.

本文提出一种基于分类方法的新型词汇替换基准测试数据集，与现有基准测试相比，该数据集具有更高的数据质量和更广的数据覆盖率，其替换词以人类判断的可行性和上下文适宜度为导向，相比已有基准测试，其能够为同一任务提供更多、更合适的替换词。