Mirella Lapata

Discourse Representation Structure Parsing

2018

We introduce an open-domain neural semantic parser which generates formal meaning representations in the style of Discourse Representation Theory (DRT; Kamp and Reyle 1993). We propose a method which transforms Discourse Representation Structures (DRSs) to …

Neural Summarization by Extracting Sentences and Words

2016 · arXiv (Cornell University)

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 sentence features. We develop a general framework for single-document summarization composed …

Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

2018 · arXiv (Cornell University)

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 to create a short, one-sentence news summary answering the question …

Query Focused Multi-Document Summarization with Distant Supervision

2020 · arXiv (Cornell University)

We consider the problem of better modeling query-cluster interactions to facilitate query focused multi-document summarization (QFS). Due to the lack of training data, existing work relies heavily on retrieval-style methods for estimating the relevance between …

A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation

2022 · arXiv (Cornell University)

We propose Composition Sampling, a simple but effective method to generate diverse outputs for conditional generation of higher quality compared to previous stochastic decoding strategies. It builds on recently proposed plan-based neural generation models (Narayan …

Explainable Abuse Detection as Intent Classification and Slot Filling

2022 · arXiv (Cornell University)

To proactively offer social media users a safe online experience, there is a need for systems that can detect harmful posts and promptly alert platform moderators. In order to guarantee the enforcement of a consistent …

Semantic Parsing for Conversational Question Answering over Knowledge Graphs

2023

In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge …

Language to Logical Form with Neural Attention

2016 · arXiv (Cornell University)

Semantic parsing aims at mapping natural language to machine interpretable meaning representations. Traditional approaches rely on high-quality lexicons, manually-built templates, and linguistic features which are either domain- or representation-specific. In this paper we present a …

Long Short-Term Memory-Networks for Machine Reading

2016 · arXiv (Cornell University)

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 incrementally from left to right and performs shallow …

Transforming Dependency Structures to Logical Forms for Semantic Parsing

2016 · Transactions of the Association for Computational Linguistics

The strongly typed syntax of grammar formalisms such as CCG, TAG, LFG and HPSG offers a synchronous framework for deriving syntactic structures and semantic logical forms. In contrast—partly due to the lack of a strong …

Neural Semantic Role Labeling with Dependency Path Embeddings

2016

This paper introduces a novel model for semantic role labeling that makes use of neural sequence modeling techniques.Our approach is motivated by the observation that complex syntactic structures and related phenomena, such as nested subordinations …

Sentence Simplification with Deep Reinforcement Learning

2017

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 monolingual corpora of complex and simple sentences. We address the …

Paraphrasing Revisited with Neural Machine Translation

2017

Recognizing and generating paraphrases is an important component in many natural language processing applications. A wellestablished technique for automatically extracting paraphrases leverages bilingual corpora to find meaning-equivalent phrases in a single language by "pivoting" over …

Neural Latent Extractive Document Summarization

2018

Extractive summarization models require sentence-level labels, which are usually created heuristically (e.g., with rule-based methods) given that most summarization datasets only have document-summary pairs. Since these labels might be suboptimal, we propose a latent variable …

Hierarchical Transformers for Multi-Document Summarization

2019

In this paper, we develop a neural summarization model which can effectively process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner. We represent cross-document relationships via …

Coarse-to-Fine Decoding for Neural Semantic Parsing

2018

Semantic parsing aims at mapping natural language utterances into structured meaning representations. In this work, we propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. Given an input utterance, we …

Data-to-Text Generation with Content Selection and Planning

2019

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 say and in what order. In this work, we …

Learning to Paraphrase for Question Answering

2017

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 capturing this knowledge and present a …

Text Summarization with Pretrained Encoders

2019 · arXiv (Cornell University)

Bidirectional Encoder Representations from Transformers (BERT) represents the latest incarnation of pretrained language models which have recently advanced a wide range of natural language processing tasks. In this paper, we showcase how BERT can be …