| Challenge: | Sememes are defined as the minimum semantic units of human languages . but most languages do not have sememe-based linguistic knowledge bases . a new framework is proposed to predict sememes for words in other languages based on semems . |
| Approach: | They propose a framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction. |
| Outcome: | The proposed model improves on baseline methods on real-world datasets. |
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Incorporating Chinese Characters of Words for Lexical Sememe Prediction (P18-1)
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| Challenge: | Existing methods of lexical sememe prediction rely on external context information of words to represent meaning. |
| Approach: | They propose a character-enhanced sememe prediction framework for Chinese language that takes advantage of internal character information and external context information. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on a Chinese sememe knowledge base and maintains robust performance even for low-frequency words. |
Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information (2022.findings-acl)
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| Challenge: | Existing sememe KBs only cover a few languages, which hinders the wide utilization of sememes. |
| Approach: | They propose to build a multilingual sememe KB based on a dictionary called BabelNet . they use multilingual synonyms, multilingual glosses and images to encode sememes . |
| Outcome: | The proposed model outperforms previous methods in terms of MAP and F1 scores. |
How Sememic Components Can Benefit Link Prediction for Lexico-Semantic Knowledge Graphs? (2025.emnlp-main)
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| Challenge: | Existing methods to predict missing triples in Knowledge Graphs are limited by semantic information. |
| Approach: | They propose a method to leverage sememe knowledge to enhance LP . LP is a technique that integrates structural and textual information into a Knowledge Graph . |
| Outcome: | The proposed method improves LP performance in English and Chinese . it improves on WN18RR, HN7 and CWN5, respectively . |
Language Modeling with Sparse Product of Sememe Experts (D18-1)
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| Challenge: | Existing language modeling methods rely on large-scale text data to learn the sequential patterns of words. |
| Approach: | They propose to use sememes to represent the implicit semantics behind words for language modeling . they propose to employ sememe-driven language models to fine-grained semem-level semantics . |
| Outcome: | Experiments on language modeling and the downstream application of headline generation show the effectiveness of SDLM. |
Automatic Construction of Sememe Knowledge Bases via Dictionaries (2021.findings-acl)
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| Challenge: | Sememe knowledge bases (SKBs) are used to analyze natural language processing. |
| Approach: | They propose a method to build sememe knowledge bases from an existing dictionary . they propose to use existing dictionaries to build an English and a French SKB . |
| Outcome: | The proposed method is superior to HowNet, the most widely used SKB that takes decades to build manually. |
Glyph Enhanced Chinese Character Pre-Training for Lexical Sememe Prediction (2021.findings-emnlp)
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| Challenge: | Sememes are defined as the atomic units to describe the semantic meaning of concepts. |
| Approach: | They propose a method which incorporates internal Chinese character information to help sememe prediction. |
| Outcome: | The proposed method outperforms existing non-external information models on howNet, a famous sememe knowledge base. |
MultiLexBATS: Multilingual Dataset of Lexical Semantic Relations (2024.lrec-main)
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Dagmar Gromann, Hugo Goncalo Oliveira, Lucia Pitarch, Elena-Simona Apostol, Jordi Bernad, Eliot Bytyçi, Chiara Cantone, Sara Carvalho, Francesca Frontini, Radovan Garabik, Jorge Gracia, Letizia Granata, Fahad Khan, Timotej Knez, Penny Labropoulou, Chaya Liebeskind, Maria Pia Di Buono, Ana Ostroški Anić, Sigita Rackevičienė, Ricardo Rodrigues, Gilles Sérasset, Linas Selmistraitis, Mahammadou Sidibé, Purificação Silvano, Blerina Spahiu, Enriketa Sogutlu, Ranka Stanković, Ciprian-Octavian Truică, Giedre Valunaite Oleskeviciene, Slavko Zitnik, Katerina Zdravkova
| Challenge: | Prior work has focused on analysing lexical semantic relations in word embeddings or probing pretrained language models (PLMs) with some exceptions. |
| Approach: | They propose to use a multilingual parallel dataset of lexical semantic relations adapted from BATS in 15 languages including low-resource languages such as Bambara, Lithuanian, and Albanian as an experiment on cross-lingual transfer of relational knowledge. |
| Outcome: | The proposed dataset is adapted from a BATS-based dataset in 15 languages including low-resource languages such as Bambara, Lithuanian, and Albanian. |
Cross-Linguistic Syntactic Evaluation of Word Prediction Models (2020.acl-main)
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| Challenge: | A range of studies have concluded that neural word prediction models can distinguish grammatical from ungrammatically sentences with high accuracy. |
| Approach: | They propose to use CLAMS to evaluate LSTM and multilingual BERT models. |
| Outcome: | The proposed model can learn syntax on English, French, German, Hebrew and Russian, and LSTM language models on multilingual and multilingual models. |
Unsupervised Cross-Lingual Representation Learning (P19-4)
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| Challenge: | a comprehensive survey of cutting-edge weakly-supervised and unsupervised cross-lingual word representations is presented . |
| Approach: | This tutorial provides a comprehensive survey of recent work on weakly-supervised and unsupervised cross-lingual word representations. |
| Outcome: | This tutorial provides a comprehensive survey of cutting-edge weakly-supervised and unsupervised word representations. |
Multi-Source Cross-Lingual Model Transfer: Learning What to Share (P19-1)
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| Challenge: | Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language . however, many languages lack the labeled training data necessary for training deep neural nets for varying NLP tasks. |
| Approach: | They propose a cross-lingual transfer learning method that leverages annotated data from other languages to build NLP models for a target language. |
| Outcome: | The proposed model achieves significant performance gains over prior art over multiple text classification and sequence tagging tasks including a large-scale industry dataset. |