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8 papers · 1 filter
Enhancing Financial Domain Adaptation of Language Models via Model Augmentation
Kota Tanabe, Masanori Hirano, Kazuki Matoya +3
The domain adaptation of language models, including large language models (LLMs), has become increasingly important as the use of such models continues to expand. This study demons…
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions
Sho Yokoi, Sosuke Kobayashi, Kenji Fukumizu +2
In this paper, we propose a new kernel-based co-occurrence measure that can be applied to sparse linguistic expressions (e.g., sentences) with a very short learning time, as an alt…
Addressee and Response Selection for Multilingual Conversation
Motoki Sato, Hiroki Ouch, Yuta Tsuboi
Developing conversational systems that can converse in many languages is an interesting challenge for natural language processing. In this paper, we introduce multilingual addresse…
Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations
Sosuke Kobayashi
We propose a novel data augmentation for labeled sentences called contextual augmentation. We assume an invariance that sentences are natural even if the words in the sentences are…
Unsupervised Learning of Style-sensitive Word Vectors
Reina Akama, Kento Watanabe, Sho Yokoi +2
This paper presents the first study aimed at capturing stylistic similarity between words in an unsupervised manner. We propose extending the continuous bag of words (CBOW) model (…
ESPnet: End-to-End Speech Processing Toolkit
Shinji Watanabe, Takaaki Hori, Shigeki Karita +9
This paper introduces a new open source platform for end-to-end speech processing named ESPnet. ESPnet mainly focuses on end-to-end automatic speech recognition (ASR), and adopts w…