Tokenization with Factorized Subword Encoding

• URL: http://arxiv.org/abs/2306.07764v1
• Date: Tue, 13 Jun 2023 13:27:34 GMT
• Title: Tokenization with Factorized Subword Encoding
• Authors: David Samuel and Lilja {\O}vrelid
• Abstract summary: We propose a novel tokenization method that factorizes subwords onto discrete triplets using a VQ-VAE model. Results indicate that this method is more appropriate and robust for morphological tasks than the commonly used byte-pair encoding (BPE) tokenization algorithm.
• Score: 2.538209532048867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, language models have become increasingly larger and more complex. However, the input representations for these models continue to rely on simple and greedy subword tokenization methods. In this paper, we propose a novel tokenization method that factorizes subwords onto discrete triplets using a VQ-VAE model. The effectiveness of the proposed tokenization method, referred to as the Factorizer, is evaluated on language modeling and morpho-syntactic tasks for 7 diverse languages. Results indicate that this method is more appropriate and robust for morphological tasks than the commonly used byte-pair encoding (BPE) tokenization algorithm.

Related papers

• Tokenization as Finite-State Transduction [24.19959327497118]
  We introduce a finite-state framework which can efficiently encode all possible tokenizations of a regular language. We show that Byte-Pair. Match (BPE) and MaxPiece (WordPiece) fit within this framework. An application of this is to guided generation, where the outputs of a language model are constrained to match some pattern.
  arXiv  Detail & Related papers  (2024-10-21T07:10:07Z)
• Constructing a BPE Tokenization DFA [0.0]
  Many natural language processing systems operate over tokenizations of text to address the open-vocabulary problem. We give and analyze an algorithm for the efficient construction of deterministic finite automata designed to operate directly on tokenizations produced by the popular byte pair encoding technique.
  arXiv  Detail & Related papers  (2024-05-13T11:59:24Z)
• Evaluating Subword Tokenization: Alien Subword Composition and OOV Generalization Challenge [10.721272718226848]
  We propose a combined intrinsic-extrinsic evaluation framework for subword tokenization. Intrepid evaluation is based on our new UniMorph Labeller tool that classifies subword tokenization as either morphological or alien. Our empirical findings show that the accuracy of UniMorph Labeller is 98%, and that alien tokenization leads to poorer generalizations.
  arXiv  Detail & Related papers  (2024-04-20T06:49:15Z)
• Downstream Task-Oriented Neural Tokenizer Optimization with Vocabulary Restriction as Post Processing [4.781986758380065]
  This paper proposes a method to optimize tokenization for the performance improvement of already trained downstream models. Our method generates tokenization results attaining lower loss values of a given downstream model on the training data for restricting vocabularies and trains a tokenizer reproducing the tokenization results.
  arXiv  Detail & Related papers  (2023-04-21T08:29:14Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• Hierarchical Sketch Induction for Paraphrase Generation [79.87892048285819]
  We introduce Hierarchical Refinement Quantized Variational Autoencoders (HRQ-VAE), a method for learning decompositions of dense encodings. We use HRQ-VAE to encode the syntactic form of an input sentence as a path through the hierarchy, allowing us to more easily predict syntactic sketches at test time.
  arXiv  Detail & Related papers  (2022-03-07T15:28:36Z)
• Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP [22.772546707304766]
  We show how hybrid approaches of words and characters as well as subword-based approaches based on learned segmentation have been proposed and evaluated. We conclude that there is and likely will never be a silver bullet singular solution for all applications.
  arXiv  Detail & Related papers  (2021-12-20T13:04:18Z)
• Charformer: Fast Character Transformers via Gradient-based Subword Tokenization [50.16128796194463]
  We propose a new model inductive bias that learns a subword tokenization end-to-end as part of the model. We introduce a soft gradient-based subword tokenization module (GBST) that automatically learns latent subword representations from characters. We additionally introduce Charformer, a deep Transformer model that integrates GBST and operates on the byte level.
  arXiv  Detail & Related papers  (2021-06-23T22:24:14Z)
• 2kenize: Tying Subword Sequences for Chinese Script Conversion [54.33749520569979]
  We propose a model that can disambiguate between mappings and convert between the two scripts. Our proposed method outperforms previous Chinese Character conversion approaches by 6 points in accuracy.
  arXiv  Detail & Related papers  (2020-05-07T10:53:05Z)
• Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck [52.08901549360262]
  Variational autoencoders (VAEs) are essential tools in end-to-end representation learning. VAEs tend to ignore latent variables with a strong auto-regressive decoder. We propose a principled approach to enforce an implicit latent feature matching in a more compact latent space.
  arXiv  Detail & Related papers  (2020-04-22T14:41:37Z)
• Byte Pair Encoding is Suboptimal for Language Model Pretraining [49.30780227162387]
  We analyze differences between unigram LM tokenization and byte-pair encoding (BPE) We find that the unigram LM tokenization method matches or outperforms BPE across downstream tasks and two languages. We hope that developers of future pretrained LMs will consider adopting the unigram LM method over the more prevalent BPE.
  arXiv  Detail & Related papers  (2020-04-07T21:21:06Z)

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.