Search Results for author:
Found 28 papers, 13 papers with code
Dancing in Chains: Reconciling Instruction Following and Faithfulness in Language Models
Surprisingly, we find that less is more, as training ReSet with high-quality, yet substantially smaller data (three-fold less) yields superior results.
Personalized Search Via Neural Contextual Semantic Relevance Ranking
Existing neural relevance models do not give enough consideration for query and item context information which diversifies the search results to adapt for personal preference.
STREET: A Multi-Task Structured Reasoning and Explanation Benchmark
We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark.
Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks
We show that, with consistent tokenization, the model performs better in both in-domain and out-of-domain datasets, with a notable average of +1. 7 F2 gain when a BART model is trained on SQuAD and evaluated on 8 QA datasets.
Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations
There has been great progress in unifying various table-to-text tasks using a single encoder-decoder model trained via multi-task learning (Xie et al., 2022).
Language Agnostic Multilingual Information Retrieval with Contrastive Learning
Multilingual information retrieval (IR) is challenging since annotated training data is costly to obtain in many languages.
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner
Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises.
Contrastive Document Representation Learning with Graph Attention Networks
In this work, we propose to use a graph attention network on top of the available pretrained Transformers model to learn document embeddings.
Attention-guided Generative Models for Extractive Question Answering
We propose a simple strategy to obtain an extractive answer span from the generative model by leveraging the decoder cross-attention patterns.
Multiplicative Position-aware Transformer Models for Language Understanding
Transformer models, which leverage architectural improvements like self-attention, perform remarkably well on Natural Language Processing (NLP) tasks.
Beyond [CLS] through Ranking by Generation
Generative models for Information Retrieval, where ranking of documents is viewed as the task of generating a query from a document's language model, were very successful in various IR tasks in the past.
Improve Transformer Models with Better Relative Position Embeddings
In this paper, we first review absolute position embeddings and existing methods for relative position embeddings.
Embedding-based Zero-shot Retrieval through Query Generation
In some cases, our model trained on synthetic data can even outperform the same model trained on real data
AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature
Coronavirus disease (COVID-19) has been declared as a pandemic by WHO with thousands of cases being reported each day.
TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding
Prior to the transformer era, bidirectional Long Short-Term Memory (BLSTM) has been the dominant modeling architecture for neural machine translation and question answering.
Ranked #1 on
Text Classification
on GLUE MRPC
WeNet: Weighted Networks for Recurrent Network Architecture Search
In this paper, we propose a novel way of architecture search by means of weighted networks (WeNet), which consist of a number of networks, with each assigned a weight.
Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition
The success of self-attention in NLP has led to recent applications in end-to-end encoder-decoder architectures for speech recognition.
Learning Noise-Invariant Representations for Robust Speech Recognition
Despite rapid advances in speech recognition, current models remain brittle to superficial perturbations to their inputs.
Residual Convolutional CTC Networks for Automatic Speech Recognition
Furthermore, we introduce a CTC-based system combination, which is different from the conventional frame-wise senone-based one.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
CNN-RNN: A Unified Framework for Multi-label Image Classification
While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects, scenes, actions and attributes in an image.
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question
The quality of the generated answers of our mQA model on this dataset is evaluated by human judges through a Turing Test.
Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks
The sentence generator produces one simple short sentence that describes a specific short video interval.
Bidirectional LSTM-CRF Models for Sequence Tagging
It can also use sentence level tag information thanks to a CRF layer.
Ranked #1 on
Named Entity Recognition (NER)
on FindVehicle
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering
The quality of the generated answers of our mQA model on this dataset is evaluated by human judges through a Turing Test.
Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images
In particular, we propose a transposed weight sharing scheme, which not only improves performance on image captioning, but also makes the model more suitable for the novel concept learning task.
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions.
ImageNet Large Scale Visual Recognition Challenge
The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images.
