Scaling Neural ITN for Numbers and Temporal Expressions in Tamil: Findings for an Agglutinative Low-resource Language (2023.emnlp-industry)
Copied to clipboard
| Challenge: | Inverse Text Normalisation (ITN) is a textrewriting task that converts verbalized text to written form. |
| Approach: | They propose to use a seq2seq model, a non-autoregressive text editor and a sequence tagger + rules combination to fine-tune three pre-trained neural models. |
| Outcome: | The proposed model improves with bootstrapping and data augmentation, and bootstrap alone shows a percentage improvement of 14.12 %. |
Similar Papers
Morphology-Aware Meta-Embeddings for Tamil (2021.naacl-srw)
Copied to clipboard
| Challenge: | In this work, we focus on producing morphologically enhanced word embeddings for Tamil, a highly agglutinative South Indian language with rich morphology that remains low-resource with regards to NLP tasks. |
| Approach: | They present a first-ever word analogy dataset for Tamil using a rules-based segmenter and meta-embedding techniques. |
| Outcome: | The proposed embeddings outperform baselines on the word analogy task by 16% and appear to mitigate a trade-off between semantic and morphological accuracy. |
Triples-to-isiXhosa (T2X): Addressing the Challenges of Low-Resource Agglutinative Data-to-Text Generation (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing data-to-text models are designed for the linguistic typology of English, but they are not suitable for low-resource languages. |
| Approach: | They propose a new dataset based on a subset of WebNLG that is agglutinative and low-resource data-to-text. |
| Outcome: | The proposed model outperforms existing models for isiXhosa and Finnish and fine-tunes machine translation models as the best method overall. |
Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging (D18-1)
Copied to clipboard
| Challenge: | Low-resource languages lack manual annotated data to learn basic models such as part-of-speech (POS) taggers. |
| Approach: | They propose a cross-lingual neural part-of-speech tagger that learns from disparate sources of distant supervision in a uniform framework. |
| Outcome: | The proposed model scales to hundreds of low-resource languages without access to gold annotated data. |
Neural Machine Translation for Agglutinative Languages via Data Rejuvenation (2025.acl-srw)
Copied to clipboard
| Challenge: | Recent years, advances in Neural Machine Translation (NMT) heavily rely on large-scale parallel corpora. |
| Approach: | They propose to combine fine-grained inactive sample identification with target-side rejuvenation to improve translation quality from agglutinative languages. |
| Outcome: | The proposed framework improves on four low-resource agglutinative language tasks. |
It’s Morphin’ Time! Combating Linguistic Discrimination with Inflectional Perturbations (2020.acl-main)
Copied to clipboard
| Challenge: | Existing work on societal bias in NLP focuses on race and gender . linguistic background is a unique attribute that has been largely ignored in the field . |
| Approach: | They examine linguistic background to craft plausible adversarial examples that expose biases in popular NLP models. |
| Outcome: | The proposed model improves robustness without sacrificing performance on clean data. |
Paramanu: Compact and Competitive Monolingual Language Models for Low-Resource Morphologically Rich Indian Languages (2026.acl-long)
Copied to clipboard
| Challenge: | Multilingual large language models are expensive to pretrain and suffer from imbalances across languages and datasets. |
| Approach: | They propose a family of Indian language-only autoregressive language models trained on open-source language-specific data for the five most spoken Indian languages. |
| Outcome: | The proposed model outperforms most larger models up to 8B across all five languages. |
IruMozhi: Automatically classifying diglossia in Tamil (2024.findings-naacl)
Copied to clipboard
| Challenge: | Literary Tamil is highly diglossic, with two very different registers in everyday use . Spoken Tamil is under-studied in modern NLP systems compared to Literary Tamil written in the Tamil script . |
| Approach: | They present a human-translated dataset of parallel text in Literary and Spoken Tamil. |
| Outcome: | The proposed model trains classifiers on the task of identifying which Tamil variety a text belongs to. |
OOVs in the Spotlight: How to Inflect Them? (2024.lrec-main)
Copied to clipboard
| Challenge: | Inflection is a process of word formation in which a base word form (lemma) is modified to express grammatical categories. |
| Approach: | They develop a retrograde model and two sequence-to-sequence models based on LSTM and Transformer. |
| Outcome: | The proposed systems outperform the existing systems on 9 out of 16 languages in the OOV evaluation. |
Post-ASR Correction in Hindi: Comparing Language Models and Large Language Models in Low-Resource Scenarios (2026.eacl-short)
Copied to clipboard
| Challenge: | Automatic Speech Recognition (ASR) systems for low-resource languages produce erroneous transcripts due to limited annotated data and linguistic complexity. |
| Approach: | They compare language models and large language models for post-ASR correction in Hindi . they observe a scaling trend under zero-shot ICL where mid-sized LLMs degrade performance before marginal recovery at extreme scales. |
| Outcome: | The proposed model outperforms larger models in both fine-tuning and in-context learning settings. |
Fine-grained Morphosyntactic Analysis and Generation Tools for More Than One Thousand Languages (2020.lrec-1)
Copied to clipboard
| Challenge: | Using morphosyntactic tools, we train and distribute tools for approximately one thousand languages. |
| Approach: | They train and distribute morphosyntactic tools for approximately one thousand languages. |
| Outcome: | The results show that the tools generalize well across rare and common forms alike. |