187 citations · 411 across the 15 of their papers we have counts for
15 papers
Intelligent Assistant Language Understanding On Device
Cecilia Aas, Hisham Abdelsalam, Irina Belousova +20
It has recently become feasible to run personal digital assistants on phones and other personal devices. In this paper we describe a design for a natural language understanding sys…
Conversational Semantic Parsing for Dialog State Tracking
Jianpeng Cheng, Devang Agrawal, Hector Martinez Alonso +12
We consider a new perspective on dialog state tracking (DST), the task of estimating a user's goal through the course of a dialog. By formulating DST as a semantic parsing task ove…
A Generative Model for Joint Natural Language Understanding and Generation
Bo-Hsiang Tseng, Jianpeng Cheng, Yimai Fang +1
Natural language understanding (NLU) and natural language generation (NLG) are two fundamental and related tasks in building task-oriented dialogue systems with opposite objectives…
Building a Neural Semantic Parser from a Domain Ontology
Jianpeng Cheng, Siva Reddy, Mirella Lapata
Semantic parsing is the task of converting natural language utterances into machine interpretable meaning representations which can be executed against a real-world environment suc…
Dependency Grammar Induction with a Neural Variational Transition-based Parser
Bowen Li, Jianpeng Cheng, Yang Liu +1
Dependency grammar induction is the task of learning dependency syntax without annotated training data. Traditional graph-based models with global inference achieve state-of-the-ar…
Weakly-supervised Neural Semantic Parsing with a Generative Ranker
Jianpeng Cheng, Mirella Lapata
Weakly-supervised semantic parsers are trained on utterance-denotation pairs, treating logical forms as latent. The task is challenging due to the large search space and spuriousne…
Learning an Executable Neural Semantic Parser
Jianpeng Cheng, Siva Reddy, Vijay Saraswat +1
This paper describes a neural semantic parser that maps natural language utterances onto logical forms which can be executed against a task-specific environment, such as a knowledg…
Edina: Building an Open Domain Socialbot with Self-dialogues
Ben Krause, Marco Damonte, Mihai Dobre +6
We present Edina, the University of Edinburgh's social bot for the Amazon Alexa Prize competition. Edina is a conversational agent whose responses utilize data harvested from Amazo…
A Generative Parser with a Discriminative Recognition Algorithm
Jianpeng Cheng, Adam Lopez, Mirella Lapata
Generative models defining joint distributions over parse trees and sentences are useful for parsing and language modeling, but impose restrictions on the scope of features and are…
Learning Structured Natural Language Representations for Semantic Parsing
Jianpeng Cheng, Siva Reddy, Vijay Saraswat +1
We introduce a neural semantic parser that converts natural language utterances to intermediate representations in the form of predicate-argument structures, which are induced with…
Dependency Parsing as Head Selection
Xingxing Zhang, Jianpeng Cheng, Mirella Lapata
Conventional graph-based dependency parsers guarantee a tree structure both during training and inference. Instead, we formalize dependency parsing as the problem of independently…
Neural Summarization by Extracting Sentences and Words
Jianpeng Cheng, Mirella Lapata
Traditional approaches to extractive summarization rely heavily on human-engineered features. In this work we propose a data-driven approach based on neural networks and continuous…
Long Short-Term Memory-Networks for Machine Reading
Jianpeng Cheng, Li Dong, Mirella Lapata
In this paper we address the question of how to render sequence-level networks better at handling structured input. We propose a machine reading simulator which processes text incr…
Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning
Jianpeng Cheng, Dimitri Kartsaklis
Deep compositional models of meaning acting on distributional representations of words in order to produce vectors of larger text constituents are evolving to a popular area of NLP…
Investigating the Role of Prior Disambiguation in Deep-learning Compositional Models of Meaning
Jianpeng Cheng, Dimitri Kartsaklis, Edward Grefenstette
This paper aims to explore the effect of prior disambiguation on neural network- based compositional models, with the hope that better semantic representations for text compounds c…