In this work, we revisit the Transformer-based pre-trained language models and identify two different types of information confusion in position encoding and model representations, respectively. 
Firstly, we show that in the relative position encoding, the joint modeling about relative distances and directions brings confusion between two heterogeneous information. It may make the model unable to capture the associative semantics of the same distance and the opposite directions, which in turn affects the performance of downstream tasks. Secondly, we notice the BERT with Mask Language Modeling (MLM) pre-training objective outputs similar token representations (last hidden states of different tokens) and head representations (attention weights\footnote{"attention weights" mainly refer to the dot product between Key and Query in the self-attention module.}of different heads), which may make the diversity of information expressed by different tokens and heads limited.
Motivated by the above investigation, we propose two novel techniques to improve pre-trained language models: Decoupled Directional Relative Position (DDRP) encoding and MTH\footnote{MTH is the abbreviation of our proposed MLM with Token Cosine Differentiation (TCD) and Head Cosine Differentiation (HCD) pre-training task. TCD and HCD are described in detail in sec. \ref{sec.introduction}(2) and sec.\ref{sec.CD}.} pre-training objective. 
DDRP decouples the relative distance features and the directional features in classical relative position encoding. 
MTH applies two novel auxiliary regularizers besides MLM to enlarge the dissimilarities between (a) last hidden states of different tokens, and (b) attention weights of different heads. 
These designs allow the model to capture different categories of information more clearly, as a way to alleviate information confusion in representation learning for better optimization.
Extensive experiments and ablation studies on GLUE benchmark demonstrate the effectiveness of our proposed methods.

Better Pre-Training by Reducing Representation Confusion

Please feel free to view the Findings papers at your convenience. You are welcome to leave your comment or a question in the Chat box.

Findings

findings / work in progress

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

EACL 2023

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

Haojie Zhang

1

SHORT BIO

Presentations

Better Pre-Training by Reducing Representation Confusion

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES