Search Results for author:
Found 17 papers, 9 papers with code
Red Dragon AI at TextGraphs 2020 Shared Task : LIT : LSTM-Interleaved Transformer for Multi-Hop Explanation Ranking
Explainable question answering for science questions is a challenging task that requires multi-hop inference over a large set of fact sentences.
Textgraphs-15 Shared Task System Description : Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings
Creating explanations for answers to science questions is a challenging task that requires multi-hop inference over a large set of fact sentences.
Shared Task 1 System Description : Exploring different approaches for multilingual tasks
The aim of the CASE 2021 Shared Task 1 was to detect and classify socio-political and crisis event information at document, sentence, cross-sentence, and token levels in a multilingual setting, with each of these subtasks being evaluated separately in each test language.
Discovering Black Lives Matter Events in the United States: Shared Task 3, CASE 2021
Evaluating the state-of-the-art event detection systems on determining spatio-temporal distribution of the events on the ground is performed unfrequently.
Capturing Sparks of Abstraction for the ARC Challenge
Excellent progress has been made recently in solving ARC Challenge problems.
Proving that Cryptic Crossword Clue Answers are Correct
Cryptic crossword clues are challenging cognitive tasks, for which new test sets are released on a daily basis by multiple international newspapers.
Investigating Prompt Engineering in Diffusion Models
With the spread of the use of Text2Img diffusion models such as DALL-E 2, Imagen, Mid Journey and Stable Diffusion, one challenge that artists face is selecting the right prompts to achieve the desired artistic output.
Handshakes AI Research at CASE 2021 Task 1: Exploring different approaches for multilingual tasks
The aim of the CASE 2021 Shared Task 1 (H\"urriyeto\u{g}lu et al., 2021) was to detect and classify socio-political and crisis event information at document, sentence, cross-sentence, and token levels in a multilingual setting, with each of these subtasks being evaluated separately in each test language.
Red Dragon AI at TextGraphs 2021 Shared Task: Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings
Creating explanations for answers to science questions is a challenging task that requires multi-hop inference over a large set of fact sentences.
Red Dragon AI at TextGraphs 2020 Shared Task: LIT : LSTM-Interleaved Transformer for Multi-Hop Explanation Ranking
Explainable question answering for science questions is a challenging task that requires multi-hop inference over a large set of fact sentences.
Paraphrasing with Large Language Models
Recently, large language models such as GPT-2 have shown themselves to be extremely adept at text generation and have also been able to achieve high-quality results in many downstream NLP tasks such as text classification, sentiment analysis and question answering with the aid of fine-tuning.
Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generation
The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions.
Unsupervised Natural Question Answering with a Small Model
The recent (2019-02) demonstration of the power of huge language models such as GPT-2 to memorise the answers to factoid questions raises questions about the extent to which knowledge is being embedded directly within these large models.
Scene Graph Parsing by Attention Graph
Scene graph representations, which form a graph of visual object nodes together with their attributes and relations, have proved useful across a variety of vision and language applications.
Transformer to CNN: Label-scarce distillation for efficient text classification
Significant advances have been made in Natural Language Processing (NLP) modelling since the beginning of 2018.
Relationships from Entity Stream
Relational reasoning is a central component of intelligent behavior, but has proven difficult for neural networks to learn.
Compressing Word Embeddings
Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic.
