srch

15.01.2019 · Relative positional encodings are defined on word pairs rather than a single word so they could not be added on word embeddings. That being said, it is possible to tie the positional encodings for different layers. Empirically AFAIR, tying/untying the relative positional encodings does not lead to substantial changes in terms of performance.

Jul 28, 2019 · To address this challenge, Transformer-XL employs novel relative positional encodings. In the vanilla transformer, positional encodings were depending on the index of the tokens. This positional encoding is depending on the relative distance between tokens, hence the name: relative positional encoding. In the paper this was done by expanding the simple query-key multiplication of the Attention Head’s Score.

Feb 02, 2021 · As an additional contribution, the Transformer-XL uses a new relative positional encoding formulation that generalizes to attention lengths longer than the one observed during training. Transformer-XL (meaning extra long) is a Transformer architecture that introduces the notion of recurrence to the deep self-attention network.

Along the way I give a proposal for implementing a bi-directional relative positional encoding, based on the architecture of Transformer-XL. I haven't been able ...

26.07.2019 · To counter this problem, we can use relative encodings instead of absolute ones as done in the vanilla Transformer paper. The positional encodings give us a clue or bias about where to attend....

As an additional contribution, the Transformer-XL uses a new relative positional encoding formulation that generalizes to attention lengths longer than the one ...

It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but ...

28.07.2019 · Relative Positional Encoding. Naively applying recurrence introduces another technical challenge. That is, the positional information is incoherent, and tokens from different segments have the same positional encoding, which is referred to as temporal confusion.To address this challenge, Transformer-XL employs novel relative positional encodings.

Relative Positional Encoding. In order to work with this new form of attention span, Transformer-XL ...

Background

Relative position encoding (RPE) is important for trans- ... Transformer recently has drawn great attention in com- ... RPE in Transformer-XL. Dai et al.

本文讨论一下Transformer中相对位置编码（Relative Position Embedding，RPE）的问题，首先聊聊Vanilla Transformer的编码方式为什么不包含相对位置信息，然后主要围绕三篇论文介绍在Transformer中加入RPE的方法。

01.04.2020 · 이 문제들에 대해 Transformer-XL에서 이용한 두 가지 메인 테크닉 1)recurrence 구조 2)relative positional encoding 의 효과를 증명합니다. 첫번 째 실험은 WikiText-103을 이용했는데, 이 데이터셋은 위에서 언급 했듯이 long-term dependency 모델링을 테스트하기에 적합합니다.

[NLP] Relative location encoding (2) Relative Positional EncoDings - Transformer-XL, Programmer All, we have been working hard to make a technical sharing ...

effective relative positional encoding formulation thatgeneralizestoattentionlengthslongerthanthe one observed during training. Transformer-XL obtained strong results on five datasets, varying from word-level to character-level language modeling. Transformer-XL is also able to generate relatively coherent long text arti-

The Problem with Transformer, Why Transformer-Xl?

In this blog post, the mechanism used to develop Transformer-XL will ... "relative positional encoding" which we will explain in a moment.

01.03.2021 · In this post, we will take a look at relative positional encoding, as introduced in Shaw et al (2018) and refined by Huang et al (2018). This is a topic I meant to explore earlier, but only recently was I able to really force myself to dive into this concept as I started reading about music generation with NLP language models. This is a separate topic for another post of its …

Transformer-XL: Attentive Language Models. Beyond a Fixed-Length Context ... effective relative positional encoding formulation.