Word Sense Disambiguation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

16 leaderboard tables shown for this task, 16 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 16 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

16 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 150 papers with code (1,035 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Language Models are Few-Shot Learners
  28 May 2020 67 repositories listed Syntology ran 15 of 65 samples · 50 unverified · 4 pointer-only (licence)
  By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
  23 Oct 2019 57 repositories listed Syntology ran 2 of 31 samples · 29 unverified
  Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).
• DeBERTa: Decoding-enhanced BERT with Disentangled Attention
  5 Jun 2020 14 repositories listed Syntology ran 4 of 13 samples · 9 unverified · 3 pointer-only (licence)
  Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.
• FlauBERT: Unsupervised Language Model Pre-training for French
  11 Dec 2019 7 repositories listed Syntology ran 1 of 9 samples · 8 unverified
  Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks.
• Enhancing Interpretable Clauses Semantically using Pretrained Word Representation
  14 Apr 2021 6 repositories listed
  The approach significantly enhances the performance and interpretability of TM.
• An Incremental Parser for Abstract Meaning Representation
  22 Aug 2016 4 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 3 pointer-only (licence)
  We describe a transition-based parser for AMR that parses sentences left-to-right, in linear time.
• Hungry Hungry Hippos: Towards Language Modeling with State Space Models
  28 Dec 2022 3 repositories listed Syntology ran 7 of 15 samples · 8 unverified
  First, we use synthetic language modeling tasks to understand the gap between SSMs and attention.
• ST-MoE: Designing Stable and Transferable Sparse Expert Models
  17 Feb 2022 3 repositories listed Syntology ran 5 of 5 samples · 0 unverified · 5 pointer-only (licence)
  But advancing the state-of-the-art across a broad set of natural language tasks has been hindered by training instabilities and uncertain quality during fine-tuning.
• Scaling Language Models: Methods, Analysis & Insights from Training Gopher
  8 Dec 2021 3 repositories listed
  Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
• GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge
  20 Aug 2019 3 repositories listed
  Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context.
• The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning
  23 May 2023 2 repositories listed
  Furthermore, we show that instruction tuning with CoT Collection allows LMs to possess stronger few-shot learning capabilities on 4 domain-specific tasks, resulting in an improvement of +2.
• Exploring the Benefits of Training Expert Language Models over Instruction Tuning
  7 Feb 2023 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Recently, Language Models (LMs) instruction-tuned on multiple tasks, also known as multitask-prompted fine-tuning (MT), have shown the capability to generalize to unseen tasks.
• N-Grammer: Augmenting Transformers with latent n-grams
  13 Jul 2022 2 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  Transformer models have recently emerged as one of the foundational models in natural language processing, and as a byproduct, there is significant recent interest and investment in scaling these models.
• Chinese Word Sense Embedding with SememeWSD and Synonym Set
  29 Jun 2022 2 repositories listed
  To address this limitation, we propose SememeWSD Synonym (SWSDS) model to assign a different vector to every sense of polysemous words with the help of word sense disambiguation (WSD) and synonym set in OpenHowNet.
• UL2: Unifying Language Learning Paradigms
  10 May 2022 2 repositories listed Syntology ran 0 of 16 samples · 16 unverified
  Our model also achieve strong results at in-context learning, outperforming 175B GPT-3 on zero-shot SuperGLUE and tripling the performance of T5-XXL on one-shot summarization.
• Training Compute-Optimal Large Language Models
  29 Mar 2022 2 repositories listed Syntology ran 8 of 11 samples · 3 unverified · 4 pointer-only (licence)
  We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
• Incorporating Word Sense Disambiguation in Neural Language Models
  15 Jun 2021 2 repositories listed
  We present two supervised (pre-)training methods to incorporate gloss definitions from lexical resources into neural language models (LMs).
• Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms
  25 Apr 2021 2 repositories listed
  The challenges with NLP systems with regards to tasks such as Machine Translation (MT), word sense disambiguation (WSD) and information retrieval make it imperative to have a labelled idioms dataset with classes such as…
• Can a Fruit Fly Learn Word Embeddings?
  18 Jan 2021 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  In this work we study a mathematical formalization of this network motif and apply it to learning the correlational structure between words and their context in a corpus of unstructured text, a common natural language…
• RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
  29 Oct 2020 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  In this paper, we introduce an advanced Russian general language understanding evaluation benchmark -- RussianGLUE.
• Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation
  29 Apr 2020 2 repositories listed
  Meta-learning aims to solve this problem by training a model on a large number of few-shot tasks, with an objective to learn new tasks quickly from a small number of examples.
• Word-Class Embeddings for Multiclass Text Classification
  26 Nov 2019 2 repositories listed
  Pre-trained word embeddings encode general word semantics and lexical regularities of natural language, and have proven useful across many NLP tasks, including word sense disambiguation, machine translation, and…
• Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense Disambiguation
  14 May 2019 2 repositories listed
  In this article, we tackle the issue of the limited quantity of manually sense annotated corpora for the task of word sense disambiguation, by exploiting the semantic relationships between senses such as synonymy,…
• Using Distributed Representations to Disambiguate Biomedical and Clinical Concepts
  19 Aug 2016 2 repositories listed
  In this paper, we report a knowledge-based method for Word Sense Disambiguation in the domains of biomedical and clinical text.
• SANDWiCH: Semantical Analysis of Neighbours for Disambiguating Words in Context ad Hoc
  7 Mar 2025 1 repository listed Syntology ran 0 of 2 samples · 2 unverified
  The rise of generative chat-based Large Language Models (LLMs) over the past two years has spurred a race to develop systems that promise near-human conversational and reasoning experiences.
• GlossGPT: GPT for Word Sense Disambiguation using Few-shot Chain-of-Thought Prompting
  1 Mar 2025 1 repository listed
  Lexical ambiguity is a major challenge in computational linguistic tasks, as limitations in proper sense identification lead to inefficient translation and question answering.
• Fietje: An open, efficient LLM for Dutch
  19 Dec 2024 1 repository listed
  The model is based on Phi 2, an English-centric model of 2.
• LexMatcher: Dictionary-centric Data Collection for LLM-based Machine Translation
  3 Jun 2024 1 repository listed
  The fine-tuning of open-source large language models (LLMs) for machine translation has recently received considerable attention, marking a shift towards data-centric research from traditional neural machine translation.
• Do Pretrained Contextual Language Models Distinguish between Hebrew Homograph Analyses?
  11 May 2024 1 repository listed
  We evaluate all existing models for contextualized Hebrew embeddings on a novel Hebrew homograph challenge sets that we deliver.
• 3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset
  29 Apr 2024 1 repository listed
  Experimental results show that MMT models trained on our dataset exhibit a greater ability to exploit visual information than those trained on other MMT datasets.

Syntology lines on 14 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.