Search Results for author:
Found 15 papers, 7 papers with code
Query Expansion Using Contextual Clue Sampling with Language Models
To balance these two considerations, we propose a combination of an effective filtering strategy and fusion of the retrieved documents based on the generation probability of each context.
What the DAAM: Interpreting Stable Diffusion Using Cross Attention
Large-scale diffusion neural networks represent a substantial milestone in text-to-image generation, but they remain poorly understood, lacking interpretability analyses.
When Do Flat Minima Optimizers Work?
Recently, flat-minima optimizers, which seek to find parameters in low-loss neighborhoods, have been shown to improve a neural network's generalization performance over stochastic and adaptive gradient-based optimizers.
Challenges in Generalization in Open Domain Question Answering
Recent work on Open Domain Question Answering has shown that there is a large discrepancy in model performance between novel test questions and those that largely overlap with training questions.
Controllable Abstractive Dialogue Summarization with Sketch Supervision
In this paper, we aim to improve abstractive dialogue summarization quality and, at the same time, enable granularity control.
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them
We introduce a new QA-pair retriever, RePAQ, to complement PAQ.
CorDial: Coarse-to-fine Abstractive Dialogue Summarization with Controllable Granularity
2) A simple strategy to control the granularity of the final summary.
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
We review the EfficientQA competition from NeurIPS 2020.
MKD: a Multi-Task Knowledge Distillation Approach for Pretrained Language Models
Our approach is model agnostic and can be easily applied on different future teacher model architectures.
Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling
Semantic similarity modeling is central to many NLP problems such as natural language inference and question answering.
Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling
A core problem of information retrieval (IR) is relevance matching, which is to rank documents by relevance to a user{'}s query.
Distilling Task-Specific Knowledge from BERT into Simple Neural Networks
In the natural language processing literature, neural networks are becoming increasingly deeper and complex.
Ranked #60 on
Sentiment Analysis
on SST-2 Binary classification
Generative Adversarial Network for Abstractive Text Summarization
In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization.
Ranked #5 on
Text Summarization
on CNN / Daily Mail (Anonymized)
Abstractive Text Summarization
Generative Adversarial Network
+2
Detecting "Smart" Spammers On Social Network: A Topic Model Approach
Spammer detection on social network is a challenging problem.
