Interpreters facilitate multi-lingual meetings but the affordable set of
languages is often smaller than what is needed. Automatic simultaneous speech
translation can extend the set of provided languages. We investigate if such an
automatic system should rather follow the original speaker, or an interpreter
to achieve better translation quality at the cost of increased delay.
To answer the question, we release Europarl Simultaneous Interpreting Corpus
(ESIC), 10 hours of recordings and transcripts of European Parliament speeches
in English, with simultaneous interpreting into Czech and German. We evaluate
quality and latency of speaker-based and interpreter-based spoken translation
systems from English to Czech. We study the differences in implicit
simplification and summarization of the human interpreter compared to a machine
translation system trained to shorten the output to some extent. Finally, we
perform human evaluation to measure information loss of each of these
approaches.

探究跟随原演讲者或译员进行自动同声传译的语音翻译系统能够在保持翻译质量的前提下，增加可提供的语言范围，从而通过 ESIC 进行英语到捷克语的研究，比较人类译员与机器翻译系统之间的差异，并进行人类评价以衡量信息损失。

口译迷惑：从原始语言或口译者翻译

Lost in Interpreting: Speech Translation from Source or Interpreter?

Discourse signals are often implicit, leaving it up to the interpreter to
draw the required inferences. At the same time, discourse is embedded in a
social context, meaning that interpreters apply their own assumptions and
beliefs when resolving these inferences, leading to multiple, valid
interpretations. However, current discourse data and frameworks ignore the
social aspect, expecting only a single ground truth. We present the first
discourse dataset with multiple and subjective interpretations of English
conversation in the form of perceived conversation acts and intents. We
carefully analyze our dataset and create computational models to (1) confirm
our hypothesis that taking into account the bias of the interpreters leads to
better predictions of the interpretations, (2) and show disagreements are
nuanced and require a deeper understanding of the different contextual factors.
We share our dataset and code at this http URL

研究探讨互动的语言表现往往含蓄隐晦，需要借助解释者自身的假设和信念进行推断并遵循社会背景，而目前的数据和框架往往仅考虑单一正确的解释，在此研究者提出了一份多维度和主观感性的英语对话的数据集，并设计了计算模型来证明解释者的偏见、不同的语境等因素决定了解释的细节和深度。