Predicting Word Similarity in Context with Referential Translation Machines
Ergun Biçici
Abstract
We identify the similarity between two words in English by casting the task as machine translation performance prediction (MTPP) between the words given the context and the distance between their similarities. We use referential translation machines (RTMs), which allows a common representation for training and test sets and stacked machine learning models. RTMs can achieve the top results in Graded Word Similarity in Context (GWSC) task.
Related Papers
A Translation of Probabilistic Event Calculus into Markov Decision Processes2025-07-17Function-to-Style Guidance of LLMs for Code Translation2025-07-15Speak2Sign3D: A Multi-modal Pipeline for English Speech to American Sign Language Animation2025-07-09Pun Intended: Multi-Agent Translation of Wordplay with Contrastive Learning and Phonetic-Semantic Embeddings2025-07-09Unconditional Diffusion for Generative Sequential Recommendation2025-07-08GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translation2025-07-04TransLaw: Benchmarking Large Language Models in Multi-Agent Simulation of the Collaborative Translation2025-07-01CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation2025-06-29