187 citations · 1.6k across the 127 of their papers we have counts for
10 papers · 2 filters
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…
Data-to-Text Generation with Content Selection and Planning
Ratish Puduppully, Li Dong, Mirella Lapata
Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to s…
Neural Latent Extractive Document Summarization
Xingxing Zhang, Mirella Lapata, Furu Wei +1
Extractive summarization models require sentence-level labels, which are usually created heuristically (e.g., with rule-based methods) given that most summarization datasets only h…
Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised
Stefanos Angelidis, Mirella Lapata
We present a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision (e.g., in the form of product domain…
Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
Shashi Narayan, Shay B. Cohen, Mirella Lapata
We introduce extreme summarization, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach. The idea is…
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…
Coarse-to-Fine Decoding for Neural Semantic Parsing
Li Dong, Mirella Lapata
Semantic parsing aims at mapping natural language utterances into structured meaning representations. In this work, we propose a structure-aware neural architecture which decompose…
Confidence Modeling for Neural Semantic Parsing
Li Dong, Chris Quirk, Mirella Lapata
In this work we focus on confidence modeling for neural semantic parsers which are built upon sequence-to-sequence models. We outline three major causes of uncertainty, and design…
Bootstrapping Generators from Noisy Data
Laura Perez-Beltrachini, Mirella Lapata
A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and associated texts. In th…
Ranking Sentences for Extractive Summarization with Reinforcement Learning
Shashi Narayan, Shay B. Cohen, Mirella Lapata
Single document summarization is the task of producing a shorter version of a document while preserving its principal information content. In this paper we conceptualize extractive…