187 citations · 1.6k across the 127 of their papers we have counts for
11 papers · 2 filters
Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis
Stefanos Angelidis, Mirella Lapata
We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL). Our neural model is trained on document sentiment labels, and lear…
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…
Whodunnit? Crime Drama as a Case for Natural Language Understanding
Lea Frermann, Shay B. Cohen, Mirella Lapata
In this paper we argue that crime drama exemplified in television programs such as CSI:Crime Scene Investigation is an ideal testbed for approximating real-world natural language u…
Learning to Paraphrase for Question Answering
Li Dong, Jonathan Mallinson, Siva Reddy +1
Question answering (QA) systems are sensitive to the many different ways natural language expresses the same information need. In this paper we turn to paraphrases as a means of ca…
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…
Image Pivoting for Learning Multilingual Multimodal Representations
Spandana Gella, Rico Sennrich, Frank Keller +1
In this paper we propose a model to learn multimodal multilingual representations for matching images and sentences in different languages, with the aim of advancing multilingual v…
Learning Structured Text Representations
Yang Liu, Mirella Lapata
In this paper, we focus on learning structure-aware document representations from data without recourse to a discourse parser or additional annotations. Drawing inspiration from re…
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…
Neural Extractive Summarization with Side Information
Shashi Narayan, Nikos Papasarantopoulos, Shay B. Cohen +1
Most extractive summarization methods focus on the main body of the document from which sentences need to be extracted. However, the gist of the document may lie in side informatio…
Sentence Simplification with Deep Reinforcement Learning
Xingxing Zhang, Mirella Lapata
Sentence simplification aims to make sentences easier to read and understand. Most recent approaches draw on insights from machine translation to learn simplification rewrites from…
Universal Semantic Parsing
Siva Reddy, Oscar Täckström, Slav Petrov +2
Universal Dependencies (UD) offer a uniform cross-lingual syntactic representation, with the aim of advancing multilingual applications. Recent work shows that semantic parsing can…