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Found 20 papers, 14 papers with code
IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages
Prior to this work, there was (i) no parallel training data spanning all 22 languages, (ii) no robust benchmarks covering all these languages and containing content relevant to India, and (iii) no existing translation models which support all the 22 scheduled languages of India.
Data-to-Text Generation with Content Selection and Planning
Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to say and in what order.
Data-to-text Generation with Entity Modeling
Recent approaches to data-to-text generation have shown great promise thanks to the use of large-scale datasets and the application of neural network architectures which are trained end-to-end.
Ranked #3 on
Data-to-Text Generation
on MLB Dataset
IndicBART: A Pre-trained Model for Indic Natural Language Generation
We present IndicBART, a multilingual, sequence-to-sequence pre-trained model focusing on 11 Indic languages and English.
Airavata: Introducing Hindi Instruction-tuned LLM
We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi.
Data-to-text Generation with Macro Planning
Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof.
Data-to-text Generation with Variational Sequential Planning
We consider the task of data-to-text generation, which aims to create textual output from non-linguistic input.
A Comprehensive Analysis of Adapter Efficiency
However, adapters have not been sufficiently analyzed to understand if PEFT translates to benefits in training/deployment efficiency and maintainability/extensibility.
Transition-Based Deep Input Linearization
Traditional methods for deep NLG adopt pipeline approaches comprising stages such as constructing syntactic input, predicting function words, linearizing the syntactic input and generating the surface forms.
Ranked #1 on
Data-to-Text Generation
on SR11Deep
Multi-Document Summarization with Centroid-Based Pretraining
In Multi-Document Summarization (MDS), the input can be modeled as a set of documents, and the output is its summary.
Decomposed Prompting for Machine Translation Between Related Languages using Large Language Models
This study investigates machine translation between related languages i. e., languages within the same family that share linguistic characteristics such as word order and lexical similarity.
CTQScorer: Combining Multiple Features for In-context Example Selection for Machine Translation
We learn a regression model, CTQ Scorer (Contextual Translation Quality), that selects examples based on multiple features in order to maximize the translation quality.
University of Edinburgh's submission to the Document-level Generation and Translation Shared Task
The University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with both English and German as targeted languages.
IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages
Natural Language Generation (NLG) for non-English languages is hampered by the scarcity of datasets in these languages.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
VerityMath: Advancing Mathematical Reasoning by Self-Verification Through Unit Consistency
Large Language Models (LLMs) combined with program-based solving techniques are increasingly demonstrating proficiency in mathematical reasoning.
RomanSetu: Efficiently unlocking multilingual capabilities of Large Language Models models via Romanization
This study addresses the challenge of extending Large Language Models (LLMs) to non-English languages using non-Roman scripts.
