187 citations · 1.6k across the 127 of their papers we have counts for
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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…