Aspect Sentiment Triplet Extraction (ASTE) aims to extract the spans of
aspect, opinion, and their sentiment relations as sentiment triplets. Existing
works usually formulate the span detection as a 1D token tagging problem, and
model the sentiment recognition with a 2D tagging matrix of token pairs.
Moreover, by leveraging the token representation of Pretrained Language
Encoders (PLEs) like BERT, they can achieve better performance. However, they
simply leverage PLEs as feature extractors to build their modules but never
have a deep look at what specific knowledge does PLEs contain. In this paper,
we argue that instead of further designing modules to capture the inductive
bias of ASTE, PLEs themselves contain "enough" features for 1D and 2D tagging:
(1) The token representation contains the contextualized meaning of token
itself, so this level feature carries necessary information for 1D tagging. (2)
The attention matrix of different PLE layers can further capture multi-level
linguistic knowledge existing in token pairs, which benefits 2D tagging. (3)
Furthermore, with simple transformations, these two features can also be easily
converted to the 2D tagging matrix and 1D tagging sequence, respectively. That
will further boost the tagging results. By doing so, PLEs can be natural
tagging frameworks and achieve a new state of the art, which is verified by
extensive experiments and deep analyses.

通过深入探究所含特定知识，该研究提出不必再设计模块来捕获 ASTI 的归纳偏差，奠定了 PLEs 可以作为自然标记框架用于 1D 和 2D 标记的基础，进一步提高标记结果的性能，这一方法已在实验中验证其新的最先进水平。

预训练语言编码器是方面情感三元组提取的自然标注框架

Pretrained Language Encoders are Natural Tagging Frameworks for Aspect Sentiment Triplet Extraction

Transformer-based models are widely used in natural language understanding
(NLU) tasks, and multimodal transformers have been effective in visual-language
tasks. This study explores distilling visual information from pretrained
multimodal transformers to pretrained language encoders. Our framework is
inspired by cross-modal encoders' success in visual-language tasks while we
alter the learning objective to cater to the language-heavy characteristics of
NLU. After training with a small number of extra adapting steps and finetuned,
the proposed XDBERT (cross-modal distilled BERT) outperforms pretrained-BERT in
general language understanding evaluation (GLUE), situations with adversarial
generations (SWAG) benchmarks, and readability benchmarks. We analyze the
performance of XDBERT on GLUE to show that the improvement is likely visually
grounded.

本文旨在研究如何将预训练的异模态 transformers 的视觉信息提炼给语言编码器，提出了一个被称为 XDBERT 的新框架，经过训练后在常规语言理解评估（GLUE）、有对抗生成的情况（SWAG）以及可读性基准测试上比预训练的 BERT 表现更好，并分析了 XDBERT 在 GLUE 上的表现，证明了改进很可能是基于视觉语言的。

XDBERT：从跨模态系统中提取视觉信息以改善语言理解的 BERT 知识蒸馏模型

XDBERT: Distilling Visual Information to BERT from Cross-Modal Systems to Improve Language Understanding

Representational Similarity Analysis (RSA) is a technique developed by
neuroscientists for comparing activity patterns of different measurement
modalities (e.g., fMRI, electrophysiology, behavior). As a framework, RSA has
several advantages over existing approaches to interpretation of language
encoders based on probing or diagnostic classification: namely, it does not
require large training samples, is not prone to overfitting, and it enables a
more transparent comparison between the representational geometries of
different models and modalities. We demonstrate the utility of RSA by
establishing a previously unknown correspondence between widely-employed
pretrained language encoders and human processing difficulty via eye-tracking
data, showcasing its potential in the interpretability toolbox for neural
models

论文介绍了一种称为 RSA 的技术，可以比较不同测量模式下（例如 fMRI、电生理学和行为学）的活动模式，展示了 RSA 的实用性，并通过眼动数据建立了先前未知的预先训练的语言编码器和人类处理难度之间的对应关系，展示了它作为神经模型可解释性工具箱的潜力。