mlsanityData to 7 Oct 2026

LitMind Dictionary: An Open-Source Online Dictionary

arXiv 2204.11087 ↗First posted 23 Apr 2022cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

Dictionaries can help language learners to learn vocabulary by providing definitions of words. Since traditional dictionaries present word senses as discrete items in predefined inventories, they fall short of flexibility, which is required in providing specific meanings of words in particular contexts. In this paper, we introduce the LitMind Dictionary (https://dictionary.litmind.ink), an open-source online generative dictionary that takes a word and context containing the word as input and automatically generates a definition as output. Incorporating state-of-the-art definition generation models, it supports not only Chinese and English, but also Chinese-English cross-lingual queries. Moreover, it has a user-friendly front-end design that can help users understand the query words quickly and easily. All the code and data are available at https://github.com/blcuicall/litmind-dictionary.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 32 references that are in the map. First posted on arXiv 23 Apr 2022.

Builds on

15 of 32 references.

References · 15

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  6. 2019Cross-lingual Language Model Pretrainingcited by 1,088
  7. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,028
  8. 2016Definition Modeling: Learning to define word embeddings in natural languagecited by 46
  9. 2018Conditional Generators of Words Definitionscited by 35
  10. 2019Mark my Word: A Sequence-to-Sequence Approach to Definition Modelingcited by 15
  11. 2019Incorporating Sememes into Chinese Definition Modelingcited by 13
  12. 2020VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modelingcited by 13
3 more
  1. 2015Breaking Sticks and Ambiguities with Adaptive Skip-gramcited by 11
  2. 2018xSense: Learning Sense-Separated Sparse Representations and Textual Definitions for Explainable Word Sense Networkscited by 8
  3. 2020Toward Cross-Lingual Definition Generation for Language Learnerscited by 5

Built on by

Not cited yet.