Revisiting Topic-Guided Language Models

• URL: http://arxiv.org/abs/2312.02331v1
• Date: Mon, 4 Dec 2023 20:33:24 GMT
• Title: Revisiting Topic-Guided Language Models
• Authors: Carolina Zheng, Keyon Vafa, David M. Blei
• Abstract summary: We study four topic-guided language models and two baselines, evaluating the held-out predictive performance of each model on four corpora. We find that none of these methods outperform a standard LSTM language model baseline, and most fail to learn good topics.
• Score: 20.21486464604549
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A recent line of work in natural language processing has aimed to combine language models and topic models. These topic-guided language models augment neural language models with topic models, unsupervised learning methods that can discover document-level patterns of word use. This paper compares the effectiveness of these methods in a standardized setting. We study four topic-guided language models and two baselines, evaluating the held-out predictive performance of each model on four corpora. Surprisingly, we find that none of these methods outperform a standard LSTM language model baseline, and most fail to learn good topics. Further, we train a probe of the neural language model that shows that the baseline's hidden states already encode topic information. We make public all code used for this study.

Related papers

• Language Models for Text Classification: Is In-Context Learning Enough? [54.869097980761595]
  Recent foundational language models have shown state-of-the-art performance in many NLP tasks in zero- and few-shot settings. An advantage of these models over more standard approaches is the ability to understand instructions written in natural language (prompts) This makes them suitable for addressing text classification problems for domains with limited amounts of annotated instances.
  arXiv  Detail & Related papers  (2024-03-26T12:47:39Z)
• Wave to Syntax: Probing spoken language models for syntax [16.643072915927313]
  We focus on the encoding of syntax in several self-supervised and visually grounded models of spoken language. We show that syntax is captured most prominently in the middle layers of the networks, and more explicitly within models with more parameters.
  arXiv  Detail & Related papers  (2023-05-30T11:43:18Z)
• FineDeb: A Debiasing Framework for Language Models [3.7698299781999376]
  We propose FineDeb, a two-phase debiasing framework for language models. Our results show that FineDeb offers stronger debiasing in comparison to other methods. Our framework is generalizable for demographics with multiple classes.
  arXiv  Detail & Related papers  (2023-02-05T18:35:21Z)
• Accidental Learners: Spoken Language Identification in Multilingual Self-Supervised Models [11.439430077017635]
  We find that pre-trained speech models optimally encode language discriminatory information in lower layers. We demonstrate that the embeddings obtained from these layers are significantly robust to classify unseen languages. We open-source the model through the NVIDIA NeMo toolkit.
  arXiv  Detail & Related papers  (2022-11-09T18:53:59Z)
• Language Model Pre-Training with Sparse Latent Typing [66.75786739499604]
  We propose a new pre-training objective, Sparse Latent Typing, which enables the model to sparsely extract sentence-level keywords with diverse latent types. Experimental results show that our model is able to learn interpretable latent type categories in a self-supervised manner without using any external knowledge.
  arXiv  Detail & Related papers  (2022-10-23T00:37:08Z)
• Integrating Linguistic Theory and Neural Language Models [2.870517198186329]
  I present several case studies to illustrate how theoretical linguistics and neural language models are still relevant to each other. This thesis contributes three studies that explore different aspects of the syntax-semantics interface in language models.
  arXiv  Detail & Related papers  (2022-07-20T04:20:46Z)
• Towards Zero-shot Language Modeling [90.80124496312274]
  We construct a neural model that is inductively biased towards learning human languages. We infer this distribution from a sample of typologically diverse training languages. We harness additional language-specific side information as distant supervision for held-out languages.
  arXiv  Detail & Related papers  (2021-08-06T23:49:18Z)
• Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation [127.81351683335143]
  Cross-lingual pretraining requires models to align the lexical- and high-level representations of the two languages. Previous research has shown that this is because the representations are not sufficiently aligned. In this paper, we enhance the bilingual masked language model pretraining with lexical-level information by using type-level cross-lingual subword embeddings.
  arXiv  Detail & Related papers  (2021-03-18T21:17:58Z)
• Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition [80.446770909975]
  Linguistic knowledge is of great benefit to scene text recognition. How to effectively model linguistic rules in end-to-end deep networks remains a research challenge. We propose an autonomous, bidirectional and iterative ABINet for scene text recognition.
  arXiv  Detail & Related papers  (2021-03-11T06:47:45Z)
• Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages [81.90356787324481]
  Spelling normalization for low resource languages is a challenging task because the patterns are hard to predict. This work shows a comparison of a neural model and character language models with varying amounts on target language data. Our usage scenario is interactive correction with nearly zero amounts of training examples, improving models as more data is collected.
  arXiv  Detail & Related papers  (2020-10-20T17:31:07Z)
• Learning Spoken Language Representations with Neural Lattice Language Modeling [39.50831917042577]
  We propose a framework that trains neural lattice language models to provide contextualized representations for spoken language understanding tasks. The proposed two-stage pre-training approach reduces the demands of speech data and has better efficiency.
  arXiv  Detail & Related papers  (2020-07-06T10:38:03Z)

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.