Search Results for author:
Found 9 papers, 1 papers with code
Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization
Inspired by these approaches for a single task setting, this paper proposes to use the finite differences first-order algorithm to calculate this gradient from dot-product of gradients, allowing explicit control on the weightage of this component relative to standard gradients.
Ranked #1 on
Text Summarization
on GigaWord
(using extra training data)
Transformers Get Stable: An End-to-End Signal Propagation Theory for Language Models
In spite of their huge success, transformer models remain difficult to scale in depth.
FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering
Using our proposed method, we outperform the current state-of-the-art method by $2. 5$ Exact Match score on the Natural Question dataset while using only $25\%$ of parameters and $35\%$ of the latency during inference, and $4. 4$ Exact Match on WebQuestions dataset.
Ranked #1 on
Question Answering
on WebQuestions
(using extra training data)
You Only Need One Model for Open-domain Question Answering
Recent approaches to Open-domain Question Answering refer to an external knowledge base using a retriever model, optionally rerank passages with a separate reranker model and generate an answer using another reader model.
Keep Learning: Self-supervised Meta-learning for Learning from Inference
A common approach in many machine learning algorithms involves self-supervised learning on large unlabeled data before fine-tuning on downstream tasks to further improve performance.
Ranked #1 on
Answer Selection
on Ubuntu Dialogue (v2, Ranking)
Learning to Generate Questions by Recovering Answer-containing Sentences
We evaluate our method against existing ones in terms of the quality of generated questions as well as the fine-tuned MRC model accuracy after training on the data synthetically generated by our method.
Ranked #3 on
Question Generation
on SQuAD1.1
(using extra training data)
NeurQuRI: Neural Question Requirement Inspector for Answerability Prediction in Machine Reading Comprehension
Real-world question answering systems often retrieve potentially relevant documents to a given question through a keyword search, followed by a machine reading comprehension (MRC) step to find the exact answer from them.
ASGen: Answer-containing Sentence Generation to Pre-Train Question Generator for Scale-up Data in Question Answering
We evaluate the question generation capability of our method by comparing the BLEU score with existing methods and test our method by fine-tuning the MRC model on the downstream MRC data after training on synthetic data.
