Emergent Mind

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

(1901.02860)

Published Jan 9, 2019 in cs.LG , cs.CL , and stat.ML

Abstract

Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch.

Comparison of Transformer-XL and RNNs in handling long-term dependencies in sequential data processing.

Overview

Transformer-XL introduces novelties in tackling the limitations of fixed-length contexts in traditional Transformers by incorporating mechanisms for reusing hidden states, enabling better memory and contextual understanding.
It features relative positional encodings instead of absolute, enhancing its capability to manage positional continuity over reused segments, thus avoiding temporal confusion.
The model has achieved state-of-the-art performance on multiple benchmarks, proving its effectiveness in handling both short and long-term dependencies and showcasing faster evaluation times beneficial for production deployment.

Exploring Transformer-XL for Language Modeling

Introduction to Context and Dependencies in Language Modeling

In language modeling, one core challenge is capturing dependencies across long stretches of text. Traditional Recurrent Neural Networks (RNNs), especially Long Short-Term Memory (LSTM) networks, have been pivotal due to their ability to remember information over lengthy sequences. However, these models often struggle with optimization issues such as gradient vanishing and exploding, limiting their effectiveness over very long contexts.

Meanwhile, models based on attention mechanisms, like Transformers, offer direct connections between distant words. This architecture can potentially learn relationships over longer spans of text more efficiently, but standard implementations have been hampered when dealing with extremely long documents, primarily due to a phenomenon known as "context fragmentation."

The Birth of Transformer-XL

To tackle the limitations posed by fixed-length context windows typical in standard Transformers, a novel architecture named Transformer-XL has been introduced. This model brings two significant innovations:

Introducing Recurrence in Transformers: Transformer-XL incorporates a mechanism that reuses the hidden states from previous segments, allowing the model to maintain a longer memory, thus facilitating better contextual understanding over long texts.
Adopting Relative Positional Encodings: Unlike traditional Transformers that use absolute positional encodings, Transformer-XL employs relative positional encodings. This adjustment makes it possible to retain positional information even when segments are reused, preventing the temporal confusion caused by standard positional encoding methods.

Impactful Results

Transformer-XL has demonstrated impressive results across multiple benchmarks:

It achieved state-of-the-art perplexity scores on several datasets including enwiki8, text8, WikiText-103, One Billion Word, and Penn Treebank.
For instance, it reduced perplexity to 18.3 on WikiText-103 and reached below 1.0 bits per character (bpc) on enwiki8.
These results signify not only substantial improvements over traditional RNNs and standard Transformers but also underline the effectiveness of Transformer-XL in managing both short and long-term dependencies efficiently.

Practical and Theoretical Implications

The introduction of Transformer-XL could have numerous implications for the field of natural language processing and beyond:

Enhanced Language Models: By better capturing long-term dependencies, Transformer-XL can vastly improve the coherence and relevance of generated text, which is crucial for applications like summarization, dialogue systems, and more.
Inspirations for New Architectures: The methodology of integrating recurrence into attention-based models opens up avenues for future innovations in network design.
Efficiency Gains: Transformer-XL is also much faster during evaluations, thanks to its state reuse mechanism, which can significantly speed up the deployment phase of language models in production environments.

Speculations on Future AI Developments

Looking ahead, the techniques pioneered in Transformer-XL might inspire more sophisticated models that either extend the context window further or utilize memory even more efficiently. Also, these approaches could be adapted to other types of sequential data beyond text, such as audio or video, potentially paving the way for more robust multimedia processing models.

Overall, Transformer-XL marks a significant step forward in our quest to model human language more effectively, demonstrating the power of combining traditional neural mechanisms with innovative adaptations, thus setting the stage for future advancements in the field.