Idiomatic expressions are an integral part of natural language and constantly
being added to a language. Owing to their non-compositionality and their
ability to take on a figurative or literal meaning depending on the sentential
context, they have been a classical challenge for NLP systems. To address this
challenge, we study the task of detecting whether a sentence has an idiomatic
expression and localizing it. Prior art for this task had studied specific
classes of idiomatic expressions offering limited views of their
generalizability to new idioms. We propose a multi-stage neural architecture
with the attention flow mechanism for identifying these expressions. The
network effectively fuses contextual and lexical information at different
levels using word and sub-word representations. Empirical evaluations on three
of the largest benchmark datasets with idiomatic expressions of varied
syntactic patterns and degrees of non-compositionality show that our proposed
model achieves new state-of-the-art results. A salient feature of the model is
its ability to identify idioms unseen during training with gains from 1.4% to
30.8% over competitive baselines on the largest dataset.

本研究提出了一种多阶段深度神经网络架构，利用注意力机制和上下文信息对句子中的习语表达进行定位，通过评估表明该模型能够在各类习语表达的数据集上实现新的最优结果，并具有对未见过的习语表达的识别能力。

使用语义兼容性进行惯用语表达式识别

Idiomatic Expression Identification using Semantic Compatibility

Real-world scenarios demand reasoning about process, more than final outcome
prediction, to discover latent causal chains and better understand complex
systems. It requires the learning algorithms to offer both accurate predictions
and clear interpretations. We design a set of trajectory reasoning tasks on
graphs with only the source and the destination observed. We present the
attention flow mechanism to explicitly model the reasoning process, leveraging
the relational inductive biases by basing our models on graph networks. We
study the way attention flow can effectively act on the underlying information
flow implemented by message passing. Experiments demonstrate that the attention
flow driven by and interacting with graph networks can provide higher accuracy
in prediction and better interpretation for trajectory reasoning.

研究了处理现实场景需求的过程推理，通过设计了一组图形轨迹推理任务，基于图形网络，通过关注流机制提高准确性和解释性。