Lifelong topic models mainly focus on in-domain text streams in which each chunk only contains documents from a single domain. To overcome data diversity of the in-domain corpus, most of the existing methods exploit the information from limited sources in a separate and heuristic manner. In this study, we develop a lifelong collaborative model (LCM) based on non-negative matrix factorization to accurately learn topics and domain-specific word embeddings. LCM particularly investigates: (1) developing a knowledge graph based on the semantic relationships among words in the lifelong learning process, so as to accumulate global context information discovered by topic models and local context information reflected by context word embeddings from previous domains, and (2) developing a subword graph based on byte pair encoding and pairwise word relationships to exploit subword information of words in the current in-domain corpus. To the best of our knowledge, we are the first to collaboratively learn topics and word embeddings via lifelong learning. Experiments on real-world in-domain text streams validate the effectiveness of our method.

Lifelong Learning of Topics and Domain-Specific Word Embeddings

Topic taxonomy discovery aims at uncovering topics of different abstraction levels and constructing hierarchical relations between them. Unfortunately, most of prior work can hardly model semantic scopes of words and topics by holding the Euclidean embedding space assumption. What's worse, they infer asymmetric hierarchical relations by symmetric distances between topic embeddings. As a result, existing methods suffer from problems of low-quality topics at high abstraction levels and inaccurate hierarchical relations. To alleviate these problems, this paper develops a Box embedding-based Topic Model (BoxTM) that maps words and topics into the box embedding space, where the asymmetric metric is defined to properly infer hierarchical relations among topics. Additionally, our BoxTM explicitly infers upper-level topics based on correlation between specific topics through recursive clustering on topic boxes. Finally, extensive experiments validate high-quality of the topic taxonomy learned by BoxTM.

Self-supervised Topic Taxonomy Discovery in the Box Embedding Space

Findings Papers - Machine Learning for NLP

technical paper

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IJCNLP-AACL 2021

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Virtual Poster Session 2

poster

## Welcome to EMNLP 2024! 
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offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
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EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

Yuyin Lu

2

Presentations

Lifelong Learning of Topics and Domain-Specific Word Embeddings

Self-supervised Topic Taxonomy Discovery in the Box Embedding Space

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