Search Results for author: Mirella Lapata

Found 123 papers, 58 papers with code

Coarse-to-Fine Query Focused Multi-Document Summarization

no code implementations EMNLP 2020 Yumo Xu, Mirella Lapata

We consider the problem of better modeling query-cluster interactions to facilitate query focused multi-document summarization.

Document Summarization Multi-Document Summarization +1

Models and Datasets for Cross-Lingual Summarisation

1 code implementation EMNLP 2021 Laura Perez-Beltrachini, Mirella Lapata

We present a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in a target language.

Universal Discourse Representation Structure Parsing

no code implementations CL (ACL) 2021 Jiangming Liu, Shay B. Cohen, Mirella Lapata, Johan Bos

Abstract We consider the task of crosslingual semantic parsing in the style of Discourse Representation Theory (DRT) where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide learning in other languages.

Semantic Parsing

Alignment-free Cross-lingual Semantic Role Labeling

no code implementations EMNLP 2020 Rui Cai, Mirella Lapata

Cross-lingual semantic role labeling (SRL) aims at leveraging resources in a source language to minimize the effort required to construct annotations or models for a new target language.

Machine Translation Multilingual Word Embeddings +2

Scaling Structured Inference with Randomization

no code implementations7 Dec 2021 Yao Fu, Mirella Lapata

In this work, we propose a family of randomized dynamic programming (RDP) algorithms for scaling structured models to tens of thousands of latent states.

Film Trailer Generation via Task Decomposition

no code implementations16 Nov 2021 Pinelopi Papalampidi, Frank Keller, Mirella Lapata

Movie trailers perform multiple functions: they introduce viewers to the story, convey the mood and artistic style of the film, and encourage audiences to see the movie.

Disentangled Sequence to Sequence Learning for Compositional Generalization

no code implementations9 Oct 2021 Hao Zheng, Mirella Lapata

There is mounting evidence that existing neural network models, in particular the very popular sequence-to-sequence architecture, struggle with compositional generalization, i. e., the ability to systematically generalize to unseen compositions of seen components.

Machine Translation Semantic Parsing +1

Learning Opinion Summarizers by Selecting Informative Reviews

1 code implementation EMNLP 2021 Arthur Bražinskas, Mirella Lapata, Ivan Titov

Opinion summarization has been traditionally approached with unsupervised, weakly-supervised and few-shot learning techniques.

Few-Shot Learning Policy Gradient Methods +1

Memory-Based Semantic Parsing

no code implementations7 Sep 2021 Parag Jain, Mirella Lapata

We present a memory-based model for context-dependent semantic parsing.

Semantic Parsing

Aspect-Controllable Opinion Summarization

no code implementations EMNLP 2021 Reinald Kim Amplayo, Stefanos Angelidis, Mirella Lapata

Recent work on opinion summarization produces general summaries based on a set of input reviews and the popularity of opinions expressed in them.

Multi-Document Summarization withDeterminantal Point Process Attention

no code implementations Journal of Artificial Intelligence Research 2021 Laura Perez-Beltrachini, Mirella Lapata

The ability to convey relevant and diverse information is critical in multi-documentsummarization and yet remains elusive for neural seq-to-seq models whose outputs are of-ten redundant and fail to correctly cover important details.

Document Summarization Multi-Document Summarization

Structured Reordering for Modeling Latent Alignments in Sequence Transduction

1 code implementation NeurIPS 2021 Bailin Wang, Mirella Lapata, Ivan Titov

Despite success in many domains, neural models struggle in settings where train and test examples are drawn from different distributions.

Machine Translation Semantic Parsing +2

Text Generation from Discourse Representation Structures

no code implementations NAACL 2021 Jiangming Liu, Shay B. Cohen, Mirella Lapata

We propose neural models to generate text from formal meaning representations based on Discourse Representation Structures (DRSs).

Text Generation

Factorising Meaning and Form for Intent-Preserving Paraphrasing

1 code implementation ACL 2021 Tom Hosking, Mirella Lapata

We propose a method for generating paraphrases of English questions that retain the original intent but use a different surface form.

Paraphrase Generation Paraphrase Identification

Text Summarization with Latent Queries

no code implementations31 May 2021 Yumo Xu, Mirella Lapata

The availability of large-scale datasets has driven the development of neural models that create summaries from single documents, for generic purposes.

Abstractive Text Summarization Language Modelling

Zero-Shot Cross-lingual Semantic Parsing

no code implementations15 Apr 2021 Tom Sherborne, Mirella Lapata

Recent work in crosslingual semantic parsing has successfully applied machine translation to localize accurate parsing to new languages.

Cross-Lingual Transfer Machine Translation +2

Learning from Executions for Semantic Parsing

1 code implementation NAACL 2021 Bailin Wang, Mirella Lapata, Ivan Titov

Based on the observation that programs which correspond to NL utterances must be always executable, we propose to encourage a parser to generate executable programs for unlabeled utterances.

Semantic Parsing

Data-to-text Generation with Macro Planning

1 code implementation4 Feb 2021 Ratish Puduppully, Mirella Lapata

Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof.

Data-to-Text Generation

Generating Query Focused Summaries from Query-Free Resources

1 code implementation ACL 2021 Yumo Xu, Mirella Lapata

The availability of large-scale datasets has driven the development of neural models that create generic summaries from single or multiple documents.

Language Modelling

Movie Summarization via Sparse Graph Construction

1 code implementation14 Dec 2020 Pinelopi Papalampidi, Frank Keller, Mirella Lapata

We summarize full-length movies by creating shorter videos containing their most informative scenes.

graph construction Turning Point Identification +1

Unsupervised Opinion Summarization with Content Planning

1 code implementation14 Dec 2020 Reinald Kim Amplayo, Stefanos Angelidis, Mirella Lapata

The recent success of deep learning techniques for abstractive summarization is predicated on the availability of large-scale datasets.

Abstractive Text Summarization Unsupervised Opinion Summarization

Extractive Opinion Summarization in Quantized Transformer Spaces

1 code implementation8 Dec 2020 Stefanos Angelidis, Reinald Kim Amplayo, Yoshihiko Suhara, Xiaolan Wang, Mirella Lapata

We present the Quantized Transformer (QT), an unsupervised system for extractive opinion summarization.

Extract Aspect

Meta-Learning for Domain Generalization in Semantic Parsing

no code implementations NAACL 2021 Bailin Wang, Mirella Lapata, Ivan Titov

The importance of building semantic parsers which can be applied to new domains and generate programs unseen at training has long been acknowledged, and datasets testing out-of-domain performance are becoming increasingly available.

Domain Generalization Meta-Learning +1

Compositional Generalization via Semantic Tagging

no code implementations Findings (EMNLP) 2021 Hao Zheng, Mirella Lapata

Although neural sequence-to-sequence models have been successfully applied to semantic parsing, they fail at compositional generalization, i. e., they are unable to systematically generalize to unseen compositions of seen components.

Semantic Parsing TAG

Noisy Self-Knowledge Distillation for Text Summarization

1 code implementation NAACL 2021 Yang Liu, Sheng Shen, Mirella Lapata

In this paper we apply self-knowledge distillation to text summarization which we argue can alleviate problems with maximum-likelihood training on single reference and noisy datasets.

Self-Knowledge Distillation Text Summarization

Dscorer: A Fast Evaluation Metric for Discourse Representation Structure Parsing

no code implementations ACL 2020 Jiangming Liu, Shay B. Cohen, Mirella Lapata

Discourse representation structures (DRSs) are scoped semantic representations for texts of arbitrary length.

Few-Shot Learning for Opinion Summarization

1 code implementation EMNLP 2020 Arthur Bražinskas, Mirella Lapata, Ivan Titov

In this work, we show that even a handful of summaries is sufficient to bootstrap generation of the summary text with all expected properties, such as writing style, informativeness, fluency, and sentiment preservation.

Few-Shot Learning Informativeness +1

Screenplay Summarization Using Latent Narrative Structure

2 code implementations ACL 2020 Pinelopi Papalampidi, Frank Keller, Lea Frermann, Mirella Lapata

Most general-purpose extractive summarization models are trained on news articles, which are short and present all important information upfront.

Document Summarization Extractive Summarization +2

Exploring Explainable Selection to Control Abstractive Summarization

1 code implementation24 Apr 2020 Wang Haonan, Gao Yang, Bai Yu, Mirella Lapata, Huang Heyan

A novel pair-wise matrix captures the sentence interactions, centrality, and attribute scores, and a mask with tunable attribute thresholds allows the user to control which sentences are likely to be included in the extraction.

Abstractive Text Summarization Document Summarization

Unsupervised Opinion Summarization with Noising and Denoising

1 code implementation ACL 2020 Reinald Kim Amplayo, Mirella Lapata

We create a synthetic dataset from a corpus of user reviews by sampling a review, pretending it is a summary, and generating noisy versions thereof which we treat as pseudo-review input.

Abstractive Text Summarization Denoising +1

Experience Grounds Language

2 code implementations EMNLP 2020 Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lapata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, Nicolas Pinto, Joseph Turian

Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates.

Representation Learning

Multi-Step Inference for Reasoning Over Paragraphs

no code implementations EMNLP 2020 Jiangming Liu, Matt Gardner, Shay B. Cohen, Mirella Lapata

Complex reasoning over text requires understanding and chaining together free-form predicates and logical connectives.

Query Focused Multi-Document Summarization with Distant Supervision

no code implementations6 Apr 2020 Yumo Xu, Mirella Lapata

We consider the problem of better modeling query-cluster interactions to facilitate query focused multi-document summarization (QFS).

Document Summarization Multi-Document Summarization +1

Bootstrapping a Crosslingual Semantic Parser

1 code implementation Findings of the Association for Computational Linguistics 2020 Tom Sherborne, Yumo Xu, Mirella Lapata

Considering when MT is inadequate, we also find that using our approach achieves parsing accuracy within 2% of complete translation using only 50% of training data.

Machine Translation Semantic Parsing +1

Unsupervised Opinion Summarization as Copycat-Review Generation

2 code implementations ACL 2020 Arthur Bražinskas, Mirella Lapata, Ivan Titov

At test time, when generating summaries, we force the novelty to be minimal, and produce a text reflecting consensus opinions.

Abstractive Text Summarization Review Generation +1

Semi-Supervised Semantic Role Labeling with Cross-View Training

no code implementations IJCNLP 2019 Rui Cai, Mirella Lapata

The successful application of neural networks to a variety of NLP tasks has provided strong impetus to develop end-to-end models for semantic role labeling which forego the need for extensive feature engineering.

Dependency Parsing Feature Engineering +2

Controllable Sentence Simplification: Employing Syntactic and Lexical Constraints

no code implementations10 Oct 2019 Jonathan Mallinson, Mirella Lapata

Sentence simplification aims to make sentences easier to read and understand.

Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs

1 code implementation IJCNLP 2019 Bailin Wang, Ivan Titov, Mirella Lapata

Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations, aka programs whose execution against a real-world environment produces a denotation.

Semantic Parsing

Informative and Controllable Opinion Summarization

1 code implementation EACL 2021 Reinald Kim Amplayo, Mirella Lapata

Opinion summarization is the task of automatically generating summaries for a set of reviews about a specific target (e. g., a movie or a product).

Movie Plot Analysis via Turning Point Identification

no code implementations IJCNLP 2019 Pinelopi Papalampidi, Frank Keller, Mirella Lapata

According to screenwriting theory, turning points (e. g., change of plans, major setback, climax) are crucial narrative moments within a screenplay: they define the plot structure, determine its progression and segment the screenplay into thematic units (e. g., setup, complications, aftermath).

Turning Point Identification

Multi-view Story Characterization from Movie Plot Synopses and Reviews

no code implementations EMNLP 2020 Sudipta Kar, Gustavo Aguilar, Mirella Lapata, Thamar Solorio

This paper considers the problem of characterizing stories by inferring properties such as theme and style using written synopses and reviews of movies.

TAG

Text Summarization with Pretrained Encoders

16 code implementations IJCNLP 2019 Yang Liu, Mirella Lapata

For abstractive summarization, we propose a new fine-tuning schedule which adopts different optimizers for the encoder and the decoder as a means of alleviating the mismatch between the two (the former is pretrained while the latter is not).

Abstractive Text Summarization Document Summarization +1

What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks

1 code implementation19 Jul 2019 Shashi Narayan, Shay B. Cohen, Mirella Lapata

We introduce 'extreme summarization', a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question ``What is the article about?''.

Document Summarization Extreme Summarization

Discourse Representation Parsing for Sentences and Documents

no code implementations ACL 2019 Jiangming Liu, Shay B. Cohen, Mirella Lapata

We introduce a novel semantic parsing task based on Discourse Representation Theory (DRT; Kamp and Reyle 1993).

Semantic Parsing

Sentence Centrality Revisited for Unsupervised Summarization

1 code implementation ACL 2019 Hao Zheng, Mirella Lapata

Single document summarization has enjoyed renewed interests in recent years thanks to the popularity of neural network models and the availability of large-scale datasets.

Document Summarization Representation Learning

Data-to-text Generation with Entity Modeling

2 code implementations ACL 2019 Ratish Puduppully, Li Dong, Mirella Lapata

Recent approaches to data-to-text generation have shown great promise thanks to the use of large-scale datasets and the application of neural network architectures which are trained end-to-end.

Data-to-Text Generation Representation Learning

Hierarchical Transformers for Multi-Document Summarization

1 code implementation ACL 2019 Yang Liu, Mirella Lapata

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.

Document Summarization Multi-Document Summarization

Text Generation from Knowledge Graphs with Graph Transformers

2 code implementations NAACL 2019 Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, Hannaneh Hajishirzi

Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce.

KG-to-Text Generation Knowledge Graphs +1

Syntax-aware Semantic Role Labeling without Parsing

1 code implementation TACL 2019 Rui Cai, Mirella Lapata

In this paper we focus on learning dependency aware representations for semantic role labeling without recourse to an external parser.

Semantic Role Labeling

Categorization in the Wild: Generalizing Cognitive Models to Naturalistic Data across Languages

no code implementations23 Feb 2019 Lea Frermann, Mirella Lapata

Categories such as animal or furniture are acquired at an early age and play an important role in processing, organizing, and communicating world knowledge.

Building a Neural Semantic Parser from a Domain Ontology

no code implementations25 Dec 2018 Jianpeng Cheng, Siva Reddy, Mirella Lapata

We address these challenges with a framework which allows to elicit training data from a domain ontology and bootstrap a neural parser which recursively builds derivations of logical forms.

Semantic Parsing

Structured Alignment Networks for Matching Sentences

no code implementations EMNLP 2018 Yang Liu, Matt Gardner, Mirella Lapata

We evaluate this model on two tasks, natural entailment detection and answer sentence selection, and find that modeling latent tree structures results in superior performance.

Natural Language Inference Question Answering

Data-to-Text Generation with Content Selection and Planning

2 code implementations3 Sep 2018 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 say and in what order.

Data-to-Text Generation

Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised

2 code implementations EMNLP 2018 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 labels and user-provided ratings).

Aspect Extraction Multiple Instance Learning

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

3 code implementations EMNLP 2018 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.

Document Summarization Extreme Summarization

Weakly-supervised Neural Semantic Parsing with a Generative Ranker

no code implementations CONLL 2018 Jianpeng Cheng, Mirella Lapata

Weakly-supervised semantic parsers are trained on utterance-denotation pairs, treating logical forms as latent.

Semantic Parsing

Neural Latent Extractive Document Summarization

no code implementations EMNLP 2018 Xingxing Zhang, Mirella Lapata, Furu Wei, Ming Zhou

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.

Document Summarization Extractive Document Summarization +2

Discourse Representation Structure Parsing

1 code implementation ACL 2018 Jiangming Liu, Shay B. Cohen, Mirella Lapata

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).

Question Answering Semantic Parsing

What's This Movie About? A Joint Neural Network Architecture for Movie Content Analysis

no code implementations NAACL 2018 Philip John Gorinski, Mirella Lapata

This work takes a first step toward movie content analysis by tackling the novel task of movie overview generation.

Decision Making

Coarse-to-Fine Decoding for Neural Semantic Parsing

2 code implementations ACL 2018 Li Dong, Mirella Lapata

Semantic parsing aims at mapping natural language utterances into structured meaning representations.

Semantic Parsing

Confidence Modeling for Neural Semantic Parsing

1 code implementation ACL 2018 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.

Semantic Parsing

Bootstrapping Generators from Noisy Data

1 code implementation NAACL 2018 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.

Data-to-Text Generation

Ranking Sentences for Extractive Summarization with Reinforcement Learning

1 code implementation NAACL 2018 Shashi Narayan, Shay B. Cohen, Mirella Lapata

In this paper we conceptualize extractive summarization as a sentence ranking task and propose a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective.

Document Summarization Extractive Summarization +1

Learning an Executable Neural Semantic Parser

no code implementations CL 2019 Jianpeng Cheng, Siva Reddy, Vijay Saraswat, Mirella Lapata

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 knowledge base or a database, to produce a response.

Whodunnit? Crime Drama as a Case for Natural Language Understanding

1 code implementation TACL 2018 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 understanding and the complex inferences associated with it.

Natural Language Understanding

Learning Contextually Informed Representations for Linear-Time Discourse Parsing

no code implementations EMNLP 2017 Yang Liu, Mirella Lapata

Experimental results show that our parser obtains state-of-the art performance on benchmark datasets, while being efficient (with time complexity linear in the number of sentences in the document) and requiring minimal feature engineering.

Discourse Parsing Feature Engineering +2

Learning to Paraphrase for Question Answering

no code implementations EMNLP 2017 Li Dong, Jonathan Mallinson, Siva Reddy, Mirella Lapata

Question answering (QA) systems are sensitive to the many different ways natural language expresses the same information need.

Question Answering

A Generative Parser with a Discriminative Recognition Algorithm

1 code implementation ACL 2017 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 often outperformed by discriminative models.

Constituency Parsing Language Modelling +1

Image Pivoting for Learning Multilingual Multimodal Representations

no code implementations EMNLP 2017 Spandana Gella, Rico Sennrich, Frank Keller, Mirella Lapata

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 versions of image search and image understanding.

Image Retrieval Semantic Textual Similarity

Learning Structured Text Representations

3 code implementations TACL 2018 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.

Learning Structured Natural Language Representations for Semantic Parsing

1 code implementation ACL 2017 Jianpeng Cheng, Siva Reddy, Vijay Saraswat, Mirella Lapata

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 a transition system and subsequently mapped to target domains.

Semantic Parsing

Neural Extractive Summarization with Side Information

1 code implementation14 Apr 2017 Shashi Narayan, Nikos Papasarantopoulos, Shay B. Cohen, Mirella Lapata

Most extractive summarization methods focus on the main body of the document from which sentences need to be extracted.

Document Summarization Extractive Summarization +2

Learning to Generate Product Reviews from Attributes

no code implementations EACL 2017 Li Dong, Shaohan Huang, Furu Wei, Mirella Lapata, Ming Zhou, Ke Xu

This paper presents an attention-enhanced attribute-to-sequence model to generate product reviews for given attribute information, such as user, product, and rating.

Review Generation Sentiment Analysis +1

Universal Semantic Parsing

1 code implementation EMNLP 2017 Siva Reddy, Oscar Täckström, Slav Petrov, Mark Steedman, Mirella Lapata

In this work, we introduce UDepLambda, a semantic interface for UD, which maps natural language to logical forms in an almost language-independent fashion and can process dependency graphs.

Question Answering Semantic Parsing

Dependency Parsing as Head Selection

1 code implementation EACL 2017 Xingxing Zhang, Jianpeng Cheng, Mirella Lapata

Conventional graph-based dependency parsers guarantee a tree structure both during training and inference.

Dependency Parsing

Neural Semantic Role Labeling with Dependency Path Embeddings

1 code implementation ACL 2016 Michael Roth, Mirella Lapata

This paper introduces a novel model for semantic role labeling that makes use of neural sequence modeling techniques.

Semantic Role Labeling

Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings

1 code implementation NAACL 2016 Spandana Gella, Mirella Lapata, Frank Keller

We introduce a new task, visual sense disambiguation for verbs: given an image and a verb, assign the correct sense of the verb, i. e., the one that describes the action depicted in the image.

Image Retrieval Word Sense Disambiguation

Language to Logical Form with Neural Attention

3 code implementations ACL 2016 Li Dong, Mirella Lapata

Semantic parsing aims at mapping natural language to machine interpretable meaning representations.

Semantic Parsing

Transforming Dependency Structures to Logical Forms for Semantic Parsing

1 code implementation TACL 2016 Siva Reddy, Oscar T{\"a}ckstr{\"o}m, Michael Collins, Tom Kwiatkowski, Dipanjan Das, Mark Steedman, Mirella Lapata

In contrast{---}partly due to the lack of a strong type system{---}dependency structures are easy to annotate and have become a widely used form of syntactic analysis for many languages.

Question Answering Semantic Parsing +1

A Bayesian Model of Diachronic Meaning Change

no code implementations TACL 2016 Lea Frermann, Mirella Lapata

Word meanings change over time and an automated procedure for extracting this information from text would be useful for historical exploratory studies, information retrieval or question answering.

Change Detection General Classification +2

Top-down Tree Long Short-Term Memory Networks

1 code implementation NAACL 2016 Xingxing Zhang, Liang Lu, Mirella Lapata

Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have been successfully applied to a variety of sequence modeling tasks.

Dependency Parsing Sentence Completion

Context-aware Frame-Semantic Role Labeling

1 code implementation TACL 2015 Michael Roth, Mirella Lapata

Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis.

Question Answering Scene Generation +2

Which Step Do I Take First? Troubleshooting with Bayesian Models

no code implementations TACL 2015 Annie Louis, Mirella Lapata

Online discussion forums and community question-answering websites provide one of the primary avenues for online users to share information.

Community Question Answering Information Retrieval +1

Sentence Compression as Tree Transduction

no code implementations15 Jan 2014 Trevor Anthony Cohn, Mirella Lapata

This paper presents a tree-to-tree transduction method for sentence compression.

Local Distortion Sentence Compression

Cross-lingual Annotation Projection for Semantic Roles

no code implementations15 Jan 2014 Sebastian Pado, Mirella Lapata

This article considers the task of automatically inducing role-semantic annotations in the FrameNet paradigm for new languages.

Large-scale Semantic Parsing without Question-Answer Pairs

no code implementations TACL 2014 Siva Reddy, Mirella Lapata, Mark Steedman

In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs.

Graph Matching Semantic Parsing

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