Accurately modeling idiomatic or non-compositional language has been a
longstanding challenge in Natural Language Processing (NLP). This is partly
because these expressions do not derive their meanings solely from their
constituent words, but also due to the scarcity of relevant data resources, and
their impact on the performance of downstream tasks such as machine translation
and simplification. In this paper we propose an approach to model idiomaticity
effectively using a triplet loss that incorporates the asymmetric contribution
of components words to an idiomatic meaning for training language models by
using adaptive contrastive learning and resampling miners to build an
idiomatic-aware learning objective. Our proposed method is evaluated on a
SemEval challenge and outperforms previous alternatives significantly in many
metrics.

本文提出了一种采用三元损失、自适应对比学习和重采样挖掘器来建立一个适用于训练语言模型的成语感知的学习目标的方法，用于有效地对成语性进行建模，在 SemEval 挑战赛中获得了显著的性能提升。

通过自适应对比三元损失增强多语言中的成语表示

Enhancing Idiomatic Representation in Multiple Languages via an Adaptive  Contrastive Triplet Loss

In aspect-based sentiment analysis, extracting aspect terms along with the
opinions being expressed from user-generated content is one of the most
important subtasks. Previous studies have shown that exploiting connections
between aspect and opinion terms is promising for this task. In this paper, we
propose a novel joint model that integrates recursive neural networks and
conditional random fields into a unified framework for explicit aspect and
opinion terms co-extraction. The proposed model learns high-level
discriminative features and double propagate information between aspect and
opinion terms, simultaneously. Moreover, it is flexible to incorporate
hand-crafted features into the proposed model to further boost its information
extraction performance. Experimental results on the SemEval Challenge 2014
dataset show the superiority of our proposed model over several baseline
methods as well as the winning systems of the challenge.

提出一种新的联合模型，将递归神经网络和条件随机场集成到一个统一的框架中，以明确的方式同时提取方面和观点术语，并展示其在信息提取性能上的优越性。