BanglishDepYT is a native Bengali depression detection dataset comprising 191,000 unlabeled YouTube comments and 1,000 bilingual‑annotated samples. The authors introduce Emotion‑Preserving Hybrid preprocessing (EPH), which retains emoticons, Banglish tokens, negations, and emotional elongation, improving SVM F1 from 0.7062 to 0.8570. Domain‑adaptive pretraining of SahajBERT on in‑domain comments yields the best performance (F1 = 0.8161, MCC = 0.6272), and LLM‑assisted annotation enables pseudo‑labeling that further boosts AUC from 0.8499 to 0.8731.
arXiv:2610.35114v1 Announce Type: new Abstract: Multilingual transformer models are often assumed to generalise across languages, but their effectiveness for coreference resolution in low-resource se...
arXiv:2610.20279v1 Announce Type: new Abstract: Part-of-speech tagging is a fundamental sequence labeling task and an important component for many downstream natural language processing applications....
arXiv:2606. 24825v1 Announce Type: cross Abstract: Part-of-Speech (POS) tagging is a foundational NLP task underpinning machine translation, information extraction, and syntactic parsing.
NEPSEE is a Nepali sentiment analysis benchmark created by having two annotators independently label 5,614 tweets from Nepali public figures on a five‑point scale. Only 3,189 sentences with identical polarity are kept for a three‑class version, while 766 sentences with adjacent labels are used in a five‑point task. Baseline results show NepBERTa achieving a macro‑F1 of 0.877 on the three‑class benchmark, with commercial LLMs performing similarly; on the five‑point task, Claude Opus 5.5 outperforms encoders by 0.10–0.12 macro‑F1, and a tokenizer audit reveals that restoring Devanagari vowel signs can significantly alter performance.
arXiv:2607. 03981v1 Announce Type: cross Abstract: Memes have become influential communication tools on social media, combining viral visuals with concise messaging to convey impactful ideas.
Bengali, spoken by over 230 million people, remains severely under-resourced in spoken language research. Existing open Bengali ASR corpora are confined to short, read or crowd-sourced speech and lack...
Lipi-Ghor is a large-scale, weakly-supervised Bengali speech corpus containing 885 hours of audio from 998 YouTube videos across 17 domains, each annotated in SSTT format with speaker diarization and timestamps. It is the first Bengali corpus to combine extensive coverage with speaker-diarized, long-form audio annotations, and the authors provide a replicable pipeline for building similar resources in other languages. The dataset includes a human-annotated gold evaluation split and is released under CC‑BY‑4.0, with raw audio withheld.
arXiv:2606. 12392v1 Announce Type: cross Abstract: Recently, large language models (LLMs) have achieved promising progress in the fields of classical Chinese translation and the generation of classical poetry.
arXiv:2608.22922v1 Announce Type: new Abstract: We present HelaBERT, a family of two BERT-based masked language models pre-trained from scratch on approximately 1 billion tokens of Sinhala text sourc...
Recently, large language models (LLMs) have achieved promising progress in the fields of classical Chinese translation and the generation of classical poetry. However, domain-specific research on precise translation and affective-semantic understanding of classical poetry remains limited.
arXiv:2609.09974v1 Announce Type: new Abstract: Grapheme-to-phoneme conversion (G2P) refers to the task of converting a sequence of graphemes to a corresponding sequence of phonemes. While Filipino G...
The paper presents a corpus of 1,262 Classical Tamil verse‑commentary pairs and evaluates several neural representation learning models—including recurrent, Transformer, Siamese, mBART‑style encoder‑decoder, and decoder‑only language models—against a TF‑IDF baseline. Experiments reveal limited gains: token‑F1 scores range from 0.02 to 0.20, the encoder‑decoder continues to lower training loss even after validation loss rises, and the decoder‑only model only reproduces authentic word order in 95.5% of minimal‑pair tests but fails to generate held‑out commentary content. The authors release the extraction and evaluation protocol while noting that redistribution of the source commentaries requires permission.