Crosslingual conditional generation (e.g., machine translation) has long
enjoyed the benefits of scaling. Nonetheless, there are still issues that scale
alone may not overcome. A source query in one language, for instance, may yield
several translation options in another language without any extra context. Only
one translation could be acceptable however, depending on the translator's
preferences and goals. Choosing the incorrect option might significantly affect
translation usefulness and quality. We propose a novel method interactive-chain
prompting -- a series of question, answering and generation intermediate steps
between a Translator model and a User model -- that reduces translations into a
list of subproblems addressing ambiguities and then resolving such subproblems
before producing the final text to be translated. To check ambiguity resolution
capabilities and evaluate translation quality, we create a dataset exhibiting
different linguistic phenomena which leads to ambiguities at inference for four
languages. To encourage further exploration in this direction, we release all
datasets. We note that interactive-chain prompting, using eight interactions as
exemplars, consistently surpasses prompt-based methods with direct access to
background information to resolve ambiguities.

提出了一种交互式链式提示方法，通过一系列问题、回答和生成步骤来解决跨语言条件生成中的翻译歧义问题，并通过制作数据集来评估其解决模糊性的能力和评估翻译质量。

交互链提示：交互实现跨语言条件生成的歧义消解

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction

Entity linking (EL) for the rapidly growing short text (e.g. search queries
and news titles) is critical to industrial applications. Most existing
approaches relying on adequate context for long text EL are not effective for
the concise and sparse short text. In this paper, we propose a novel framework
called Multi-turn Multiple-choice Machine reading comprehension (M3}) to solve
the short text EL from a new perspective: a query is generated for each
ambiguous mention exploiting its surrounding context, and an option selection
module is employed to identify the golden entity from candidates using the
query. In this way, M3 framework sufficiently interacts limited context with
candidate entities during the encoding process, as well as implicitly considers
the dissimilarities inside the candidate bunch in the selection stage. In
addition, we design a two-stage verifier incorporated into M3 to address the
commonly existed unlinkable problem in short text. To further consider the
topical coherence and interdependence among referred entities, M3 leverages a
multi-turn fashion to deal with mentions in a sequence manner by retrospecting
historical cues. Evaluation shows that our M3 framework achieves the
state-of-the-art performance on five Chinese and English datasets for the
real-world short text EL.

提出一种新的框架 M3，通过多轮阅读理解的方式处理短文本中的实体链接问题，借助其上下文生成查询并使用选项选择模块识别黄金实体，同时考虑候选实体的差异性和历史线索。实验结果表明，M3 框架在现实世界短文本实体链接的五个中英文数据集上取得了最好的性能。

读取、回顾、选择：一种短文本实体链接的 MRC 框架

Read, Retrospect, Select: An MRC Framework to Short Text Entity Linking

The Winograd Schema Challenge is both a commonsense reasoning and natural
language understanding challenge, introduced as an alternative to the Turing
test. A Winograd schema is a pair of sentences differing in one or two words
with a highly ambiguous pronoun, resolved differently in the two sentences,
that appears to require commonsense knowledge to be resolved correctly. The
examples were designed to be easily solvable by humans but difficult for
machines, in principle requiring a deep understanding of the content of the
text and the situation it describes. This paper reviews existing Winograd
Schema Challenge benchmark datasets and approaches that have been published
since its introduction.

本文综述了 Winograd Schema Challenge，介绍了它的常识推理和自然语言理解挑战，并回顾了自提出以来已发布的数据集和方法，它是一种包含高度歧义代词的一对句子，需要深刻理解文本内容和情景以正确解决。

Winograd 模式挑战数据集和方法综述

A Review of Winograd Schema Challenge Datasets and Approaches

This paper addresses two central problems for probabilistic processing
models: parameter estimation from incomplete data and efficient retrieval of
most probable analyses. These questions have been answered satisfactorily only
for probabilistic regular and context-free models. We address these problems
for a more expressive probabilistic constraint logic programming model. We
present a log-linear probability model for probabilistic constraint logic
programming. On top of this model we define an algorithm to estimate the
parameters and to select the properties of log-linear models from incomplete
data. This algorithm is an extension of the improved iterative scaling
algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm
applies to log-linear models in general and is accompanied with suitable
approximation methods when applied to large data spaces. Furthermore, we
present an approach for searching for most probable analyses of the
probabilistic constraint logic programming model. This method can be applied to
the ambiguity resolution problem in natural language processing applications.

本文基于概率约束编程模型，提出了一种基于对数线性概率模型的参数估计和查询概率约束编程模型中最可能的分析方法，并提供了适用于大数据空间的合适的近似方法。