Search Results for author:
Found 14 papers, 4 papers with code
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.
Span-Selective Linear Attention Transformers for Effective and Robust Schema-Guided Dialogue State Tracking
We demonstrate the effectiveness of our model on the Schema-Guided Dialogue (SGD) and MultiWOZ datasets.
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.
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)
SLM: Learning a Discourse Language Representation with Sentence Unshuffling
We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation in a fully self-supervised manner.
Answering Open-Domain Questions of Varying Reasoning Steps from Text
We develop a unified system to answer directly from text open-domain questions that may require a varying number of retrieval steps.
Ranked #9 on
Question Answering
on HotpotQA
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.
On-Device Neural Language Model Based Word Prediction
Recent developments in deep learning with application to language modeling have led to success in tasks of text processing, summarizing and machine translation.
A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension
Reading Comprehension (RC) of text is one of the fundamental tasks in natural language processing.
Ranked #69 on
Question Answering
on SQuAD1.1
Syllable-level Neural Language Model for Agglutinative Language
Language models for agglutinative languages have always been hindered in past due to myriad of agglutinations possible to any given word through various affixes.
An Embedded Deep Learning based Word Prediction
Recent developments in deep learning with application to language modeling have led to success in tasks of text processing, summarizing and machine translation.
