arXiv AI
PatTransBench: an ipc-stratified, multi-dimensional benchmark for patent machine translation evaluation
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arXiv AI
Oct 15
PATMT2: Two-stage domain adaptation for multilingual patent translation
arXiv:2610.16466v1 Announce Type: cross Abstract: We present PatMT2 (Patent Machine Translation, v2), a two-stage LoRA pipeline that adapts the Hy-MT2-7B dedicated translation model to four-language...
arXiv Computation and Language
Sep 2
Unified Multi-Dialectal Neural Machine Translation for Bangla Using the Dwadash Benchmark Corpus
arXiv:2608.12018v2 Announce Type: replace Abstract: Neural Machine Translation (NMT) and Large Language Models (LLMs) excel at cross-lingual tasks but often fail to capture intra-lingual morphologica...
arXiv Computation and Language
Sep 22
ChemCLIR-Bench: Benchmarking Cross-Lingual Information Retrieval in Multilingual Chemical Patents
arXiv:2609.23231v1 Announce Type: new Abstract: Cross-lingual information retrieval (CLIR) is increasingly important in multi-national industries, where critical technical evidence may exist in a dif...
arXiv Computation and Language
1d ago
Dataset Collection and Fine-Tuning of NLLB for German--English Translation
arXiv:2611.07797v1 Announce Type: new Abstract: This paper describes the curation of a high-quality parallel corpus from OPUS Books and the fine-tuning of Meta's NLLB-200 models for German--English t...
arXiv Computation and Language
Sep 2
ALEE: Any-Language Evaluation of Embeddings via English-Centric Minimal Pairs
arXiv:2607.00171v2 Announce Type: replace Abstract: Text embeddings are standard for semantic similarity tasks, yet their evaluation remains an open challenge. Current benchmarks are static, cover on...
arXiv Computation and Language
Oct 22
From prompting to evidence-based translation: A RAG+prompt system for Japanese-Chinese translation and its pedagogical potential
The paper presents a retrieval‑augmented generation (RAG) system combined with prompt engineering for Japanese‑to‑Chinese translation, focusing on sentences with noun‑modifying clause constructions (NMCCs). It integrates linguistic analysis, embedding‑based retrieval, and prompt construction before generating translations with GPT‑4o, without altering the base model. Experiments on a 66‑sentence test set show that increasing the knowledge‑base size from 0 to 2,000 examples raises macro‑averaged BLEU scores from 24.28 to 29.96, indicating improved translation quality for NMCC‑rich sentences.