Towards Incremental Transformers: An Empirical Analysis of Transformer Models for Incremental NLU

• URL: http://arxiv.org/abs/2109.07364v2
• Date: Thu, 2 May 2024 12:50:57 GMT
• Title: Towards Incremental Transformers: An Empirical Analysis of Transformer Models for Incremental NLU
• Authors: Patrick Kahardipraja, Brielen Madureira, David Schlangen,
• Abstract summary: Incremental processing allows interactive systems to respond based on partial inputs. Recent work attempts to apply Transformers incrementally via restart-incrementality. This approach is computationally costly and does not scale efficiently for long sequences.
• Score: 19.103130032967663
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Incremental processing allows interactive systems to respond based on partial inputs, which is a desirable property e.g. in dialogue agents. The currently popular Transformer architecture inherently processes sequences as a whole, abstracting away the notion of time. Recent work attempts to apply Transformers incrementally via restart-incrementality by repeatedly feeding, to an unchanged model, increasingly longer input prefixes to produce partial outputs. However, this approach is computationally costly and does not scale efficiently for long sequences. In parallel, we witness efforts to make Transformers more efficient, e.g. the Linear Transformer (LT) with a recurrence mechanism. In this work, we examine the feasibility of LT for incremental NLU in English. Our results show that the recurrent LT model has better incremental performance and faster inference speed compared to the standard Transformer and LT with restart-incrementality, at the cost of part of the non-incremental (full sequence) quality. We show that the performance drop can be mitigated by training the model to wait for right context before committing to an output and that training with input prefixes is beneficial for delivering correct partial outputs.

Related papers

• SPION: Layer-Wise Sparse Training of Transformer via Convolutional Flood Filling [1.0128808054306186]
  We propose a novel sparsification scheme for the Transformer that integrates convolution filters and the flood filling method. Our sparsification approach reduces the computational complexity and memory footprint of the Transformer during training. New SPION achieves up to 3.08X speedup over existing state-of-the-art sparse Transformer models.
  arXiv  Detail & Related papers  (2023-09-22T02:14:46Z)
• TAPIR: Learning Adaptive Revision for Incremental Natural Language Understanding with a Two-Pass Model [14.846377138993645]
  Recent neural network-based approaches for incremental processing mainly use RNNs or Transformers. A restart-incremental interface that repeatedly passes longer input prefixes can be used to obtain partial outputs, while providing the ability to revise. We propose the Two-pass model for AdaPtIve Revision (TAPIR) and introduce a method to obtain an incremental supervision signal for learning an adaptive revision policy.
  arXiv  Detail & Related papers  (2023-05-18T09:58:19Z)
• ByteTransformer: A High-Performance Transformer Boosted for Variable-Length Inputs [6.9136984255301]
  We present ByteTransformer, a high-performance transformer boosted for variable-length inputs. ByteTransformer surpasses the state-of-the-art Transformer frameworks, such as PyTorch JIT, XLA, Tencent TurboTransformer and NVIDIA FasterTransformer.
  arXiv  Detail & Related papers  (2022-10-06T16:57:23Z)
• Linearizing Transformer with Key-Value Memory Bank [54.83663647680612]
  We propose MemSizer, an approach to project the source sequence into lower dimension representation. MemSizer not only achieves the same linear time complexity but also enjoys efficient recurrent-style autoregressive generation. We demonstrate that MemSizer provides an improved tradeoff between efficiency and accuracy over the vanilla transformer.
  arXiv  Detail & Related papers  (2022-03-23T18:10:18Z)
• Sparse is Enough in Scaling Transformers [12.561317511514469]
  Large Transformer models yield impressive results on many tasks, but are expensive to train, or even fine-tune, and so slow at decoding that their use and study becomes out of reach. We propose Scaling Transformers, a family of next generation Transformer models that use sparse layers to scale efficiently and perform unbatched decoding much faster than the standard Transformer.
  arXiv  Detail & Related papers  (2021-11-24T19:53:46Z)
• Finetuning Pretrained Transformers into RNNs [81.72974646901136]
  Transformers have outperformed recurrent neural networks (RNNs) in natural language generation. A linear-complexity recurrent variant has proven well suited for autoregressive generation. This work aims to convert a pretrained transformer into its efficient recurrent counterpart.
  arXiv  Detail & Related papers  (2021-03-24T10:50:43Z)
• Shortformer: Better Language Modeling using Shorter Inputs [62.51758040848735]
  We show that initially training the model on short subsequences, before moving on to longer ones, both reduces overall training time. We then show how to improve the efficiency of recurrence methods in transformers.
  arXiv  Detail & Related papers  (2020-12-31T18:52:59Z)
• Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing [112.2208052057002]
  We propose Funnel-Transformer which gradually compresses the sequence of hidden states to a shorter one. With comparable or fewer FLOPs, Funnel-Transformer outperforms the standard Transformer on a wide variety of sequence-level prediction tasks.
  arXiv  Detail & Related papers  (2020-06-05T05:16:23Z)
• Applying the Transformer to Character-level Transduction [68.91664610425114]
  The transformer has been shown to outperform recurrent neural network-based sequence-to-sequence models in various word-level NLP tasks. We show that with a large enough batch size, the transformer does indeed outperform recurrent models for character-level tasks.
  arXiv  Detail & Related papers  (2020-05-20T17:25:43Z)
• Addressing Some Limitations of Transformers with Feedback Memory [51.94640029417114]
  Transformers have been successfully applied to sequential, auto-regressive tasks despite being feedforward networks. We propose the Feedback Transformer architecture that exposes all previous representations to all future representations. We demonstrate on a variety of benchmarks in language modeling, machine translation, and reinforcement learning that the increased representation capacity can create small, shallow models with much stronger performance than comparable Transformers.
  arXiv  Detail & Related papers  (2020-02-21T16:37:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.