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Related papers: Controlling Complexity in Part-of-Speech Induction

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Pre-trained word embeddings improve the performance of a neural model at the cost of increasing the model size. We propose to benefit from this resource without paying the cost by operating strictly at the sub-lexical level. Our approach is…

Computation and Language · Computer Science 2017-07-24 Karl Stratos

We introduce categorical modularity, a novel low-resource intrinsic metric to evaluate word embedding quality. Categorical modularity is a graph modularity metric based on the $k$-nearest neighbor graph constructed with embedding vectors of…

Computation and Language · Computer Science 2021-06-03 Sílvia Casacuberta , Karina Halevy , Damián E. Blasi

Automatic Speech Recognition (ASR) systems can be trained to achieve remarkable performance given large amounts of manually transcribed speech, but large labeled data sets can be difficult or expensive to acquire for all languages of…

Computation and Language · Computer Science 2022-03-22 Hanan Aldarmaki , Asad Ullah , Nazar Zaki

We consider the problem of aligning continuous word representations, learned in multiple languages, to a common space. It was recently shown that, in the case of two languages, it is possible to learn such a mapping without supervision.…

Computation and Language · Computer Science 2019-06-06 Jean Alaux , Edouard Grave , Marco Cuturi , Armand Joulin

Embedding paralinguistic properties is a challenging task as there are only a few hours of training data available for domains such as emotional speech. One solution to this problem is to pretrain a general self-supervised speech…

Computation and Language · Computer Science 2022-11-04 Florian Lux , Ching-Yi Chen , Ngoc Thang Vu

We introduce a noisy channel approach for language model prompting in few-shot text classification. Instead of computing the likelihood of the label given the input (referred as direct models), channel models compute the conditional…

Computation and Language · Computer Science 2022-03-16 Sewon Min , Mike Lewis , Hannaneh Hajishirzi , Luke Zettlemoyer

Large language models (LLMs) often necessitate extensive labeled datasets and training compute to achieve impressive performance across downstream tasks. This paper explores a self-training paradigm, where the LLM autonomously curates its…

Computation and Language · Computer Science 2024-11-13 Wei Jie Yeo , Teddy Ferdinan , Przemyslaw Kazienko , Ranjan Satapathy , Erik Cambria

We propose a novel task-agnostic in-domain pre-training method that sits between generic pre-training and fine-tuning. Our approach selectively masks in-domain keywords, i.e., words that provide a compact representation of the target…

Computation and Language · Computer Science 2023-07-17 Shahriar Golchin , Mihai Surdeanu , Nazgol Tavabi , Ata Kiapour

We study the sample complexity of semi-supervised learning (SSL) and introduce new assumptions based on the mismatch between a mixture model learned from unlabeled data and the true mixture model induced by the (unknown) class conditional…

Machine Learning · Computer Science 2018-09-11 Chen Dan , Liu Leqi , Bryon Aragam , Pradeep Ravikumar , Eric P. Xing

In dictionary learning, also known as sparse coding, the algorithm is given samples of the form $y = Ax$ where $x\in \mathbb{R}^m$ is an unknown random sparse vector and $A$ is an unknown dictionary matrix in $\mathbb{R}^{n\times m}$…

Data Structures and Algorithms · Computer Science 2014-01-06 Sanjeev Arora , Aditya Bhaskara , Rong Ge , Tengyu Ma

Unsupervised representation learning for speech processing has matured greatly in the last few years. Work in computer vision and natural language processing has paved the way, but speech data offers unique challenges. As a result, methods…

Audio and Speech Processing · Electrical Eng. & Systems 2022-03-04 Lasse Borgholt , Jakob Drachmann Havtorn , Joakim Edin , Lars Maaløe , Christian Igel

This paper presents a novel approach to the acquisition of language models from corpora. The framework builds on Cobweb, an early system for constructing taxonomic hierarchies of probabilistic concepts that used a tabular, attribute-value…

Computation and Language · Computer Science 2022-12-23 Christopher J. MacLellan , Peter Matsakis , Pat Langley

Despite the remarkable advances in language modeling, current mainstream decoding methods still struggle to generate texts that align with human texts across different aspects. In particular, sampling-based methods produce less-repetitive…

Computation and Language · Computer Science 2024-06-06 Haozhe Ji , Pei Ke , Hongning Wang , Minlie Huang

To maximize the accuracy and increase the overall acceptance of text classifiers, we propose a framework for the efficient, in-operation moderation of classifiers' output. Our framework focuses on use cases in which F1-scores of modern…

Machine Learning · Computer Science 2022-04-05 Jakob Smedegaard Andersen , Walid Maalej

Zero-resource word segmentation and clustering systems aim to tokenise speech into word-like units without access to text labels. Despite progress, the induced lexicons are still far from perfect. In an idealised setting with gold word…

Audio and Speech Processing · Electrical Eng. & Systems 2026-01-28 Danel Slabbert , Simon Malan , Herman Kamper

Automatic detection of speaker confidence is critical for adaptive computing but remains constrained by limited labelled data and the subjectivity of paralinguistic annotations. This paper proposes a semi-supervised hybrid framework that…

Sound · Computer Science 2026-05-13 Adam Wynn , Jingyun Wang

Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almost avoidable. However, those methods split a word into…

Computation and Language · Computer Science 2021-05-17 Wentao Ma , Yiming Cui , Chenglei Si , Ting Liu , Shijin Wang , Guoping Hu

We study the problem of generating keyphrases that summarize the key points for a given document. While sequence-to-sequence (seq2seq) models have achieved remarkable performance on this task (Meng et al., 2017), model training often relies…

Computation and Language · Computer Science 2019-09-09 Hai Ye , Lu Wang

We address the problems of multi-domain and single-domain regression based on distinct and unpaired labeled training sets for each of the domains and a large unlabeled training set from all domains. We formulate these problems as a Bayesian…

Machine Learning · Statistics 2012-03-21 Tomer Michaeli , Yonina C. Eldar , Guillermo Sapiro

Semi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP. Nonetheless, their development has happened in isolation, while the…

Computation and Language · Computer Science 2021-01-27 Yi Zhu , Ehsan Shareghi , Yingzhen Li , Roi Reichart , Anna Korhonen
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