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Found 19 papers, 2 papers with code
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
Our evaluations show that the best model in any given evaluation reaches on average 87% of ChatGPT performance, and 73% of GPT-4 performance, suggesting that further investment in building better base models and instruction-tuning data is required to close the gap.
Continual Dialogue State Tracking via Example-Guided Question Answering
Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on previously learnt services.
Curriculum Script Distillation for Multilingual Visual Question Answering
Pre-trained models with dual and cross encoders have shown remarkable success in propelling the landscape of several tasks in vision and language in Visual Question Answering (VQA).
Multilingual Multimodality: A Taxonomical Survey of Datasets, Techniques, Challenges and Opportunities
To this end, we review the languages studied, gold or silver data with parallel annotations, and understand how these modalities and languages interact in modeling.
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.
Switch Point biased Self-Training: Re-purposing Pretrained Models for Code-Switching
Code-switching (CS), a ubiquitous phenomenon due to the ease of communication it offers in multilingual communities still remains an understudied problem in language processing.
CodemixedNLP: An Extensible and Open NLP Toolkit for Code-Mixing
The NLP community has witnessed steep progress in a variety of tasks across the realms of monolingual and multilingual language processing recently.
Grounding 'Grounding' in NLP
And finally, (3) How to advance our current definition to bridge the gap with Cognitive Science?
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Extreme Summarization
on GEM-XSum
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
Reading Between the Lines: Exploring Infilling in Visual Narratives
In this paper, we tackle this problem by using \textit{infilling} techniques involving prediction of missing steps in a narrative while generating textual descriptions from a sequence of images.
Positioning yourself in the maze of Neural Text Generation: A Task-Agnostic Survey
Neural text generation metamorphosed into several critical natural language applications ranging from text completion to free form narrative generation.
Denoising Large-Scale Image Captioning from Alt-text Data using Content Selection Models
Training large-scale image captioning (IC) models demands access to a rich and diverse set of training examples, gathered from the wild, often from noisy alt-text data.
Style Variation as a Vantage Point for Code-Switching
We believe this viewpoint of CS as style variations opens new perspectives for modeling various tasks in CS text.
Induction and Reference of Entities in a Visual Story
We also conduct human evaluation from which it is concluded that the visual stories generated by our model are preferred 82% of the times.
"My Way of Telling a Story": Persona based Grounded Story Generation
To this end, we propose five models which are incremental extensions to the baseline model to perform the task at hand.
A Survey of Code-switched Speech and Language Processing
Code-switching, the alternation of languages within a conversation or utterance, is a common communicative phenomenon that occurs in multilingual communities across the world.
Textually Enriched Neural Module Networks for Visual Question Answering
Problems at the intersection of language and vision, like visual question answering, have recently been gaining a lot of attention in the field of multi-modal machine learning as computer vision research moves beyond traditional recognition tasks.
Comparative Analysis of Neural QA models on SQuAD
The task of Question Answering has gained prominence in the past few decades for testing the ability of machines to understand natural language.
