31 citations · 147 across the 16 of their papers we have counts for
14 papers · 1 filter
EEL: Efficiently Encoding Lattices for Reranking
Prasann Singhal, Jiacheng Xu, Xi Ye +1
Standard decoding approaches for conditional text generation tasks typically search for an output hypothesis with high model probability, but this may not yield the best hypothesis…
Best- Search Algorithm for Neural Text Generation
Jiacheng Xu, Caiming Xiong, Silvio Savarese +1
Modern natural language generation paradigms require a good decoding strategy to obtain quality sequences out of the model. Beam search yields high-quality but low diversity output…
Robust Unstructured Knowledge Access in Conversational Dialogue with ASR Errors
Yik-Cheung Tam, Jiacheng Xu, Jiakai Zou +3
Performance of spoken language understanding (SLU) can be degraded with automatic speech recognition (ASR) errors. We propose a novel approach to improve SLU robustness by randomly…
Neural Architecture Search on Efficient Transformers and Beyond
Zexiang Liu, Dong Li, Kaiyue Lu +4
Recently, numerous efficient Transformers have been proposed to reduce the quadratic computational complexity of standard Transformers caused by the Softmax attention. However, mos…
Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors
Liyan Tang, Tanya Goyal, Alexander R. Fabbri +6
The propensity of abstractive summarization models to make factual errors has been studied extensively, including design of metrics to detect factual errors and annotation of error…
ASPECTNEWS: Aspect-Oriented Summarization of News Documents
Ojas Ahuja, Jiacheng Xu, Akshay Gupta +2
Generic summaries try to cover an entire document and query-based summaries try to answer document-specific questions. But real users' needs often fall in between these extremes an…
Compressive Summarization with Plausibility and Salience Modeling
Shrey Desai, Jiacheng Xu, Greg Durrett
Compressive summarization systems typically rely on a crafted set of syntactic rules to determine what spans of possible summary sentences can be deleted, then learn a model of wha…
Understanding Neural Abstractive Summarization Models via Uncertainty
Jiacheng Xu, Shrey Desai, Greg Durrett
An advantage of seq2seq abstractive summarization models is that they generate text in a free-form manner, but this flexibility makes it difficult to interpret model behavior. In t…
Discourse-Aware Neural Extractive Text Summarization
Jiacheng Xu, Zhe Gan, Yu Cheng +1
Recently BERT has been adopted for document encoding in state-of-the-art text summarization models. However, sentence-based extractive models often result in redundant or uninforma…
Neural Extractive Text Summarization with Syntactic Compression
Jiacheng Xu, Greg Durrett
Recent neural network approaches to summarization are largely either selection-based extraction or generation-based abstraction. In this work, we present a neural model for single-…
Spherical Latent Spaces for Stable Variational Autoencoders
Jiacheng Xu, Greg Durrett
A hallmark of variational autoencoders (VAEs) for text processing is their combination of powerful encoder-decoder models, such as LSTMs, with simple latent distributions, typicall…
Incorporating Discriminator in Sentence Generation: a Gibbs Sampling Method
Jinyue Su, Jiacheng Xu, Xipeng Qiu +1
Generating plausible and fluent sentence with desired properties has long been a challenge. Most of the recent works use recurrent neural networks (RNNs) and their variants to pred…
Knowledge Graph Representation with Jointly Structural and Textual Encoding
Jiacheng Xu, Kan Chen, Xipeng Qiu +1
The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces. Previously, most works focuse…
Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification
Jiacheng Xu, Danlu Chen, Xipeng Qiu +1
Recently, neural networks have achieved great success on sentiment classification due to their ability to alleviate feature engineering. However, one of the remaining challenges is…