no code implementations • EMNLP 2020 • Jonathan Mallinson, Rico Sennrich, Mirella Lapata
Sentence simplification aims to make sentences easier to read and understand.
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.
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.
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.
1 code implementation • 28 Jan 2023 • Laura Perez-Beltrachini, Parag Jain, Emilio Monti, Mirella Lapata
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 graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities).
no code implementations • 20 Dec 2022 • Evgeniia Razumovskaia, Joshua Maynez, Annie Louis, Mirella Lapata, Shashi Narayan
We consider the problem of automatically generating stories in multiple languages.
no code implementations • 20 Dec 2022 • Roee Aharoni, Shashi Narayan, Joshua Maynez, Jonathan Herzig, Elizabeth Clark, Mirella Lapata
Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets.
no code implementations • 12 Dec 2022 • Hao Zheng, Mirella Lapata
Compositional generalization is a basic mechanism in human language learning, which current neural networks struggle with.
no code implementations • 15 Nov 2022 • Priyanka Agrawal, Chris Alberti, Fantine Huot, Joshua Maynez, Ji Ma, Sebastian Ruder, Kuzman Ganchev, Dipanjan Das, Mirella Lapata
The availability of large, high-quality datasets has been one of the main drivers of recent progress in question answering (QA).
no code implementations • 10 Oct 2022 • Pinelopi Papalampidi, Mirella Lapata
In this paper, we focus on video-to-text summarization and investigate how to best utilize multimodal information for summarizing long inputs (e. g., an hour-long TV show) into long outputs (e. g., a multi-sentence summary).
1 code implementation • 6 Oct 2022 • Agostina Calabrese, Björn Ross, Mirella Lapata
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.
1 code implementation • 26 Sep 2022 • Yumo Xu, Mirella Lapata
Extractive summarization produces summaries by identifying and concatenating the most important sentences in a document.
no code implementations • 26 Sep 2022 • Tom Sherborne, Mirella Lapata
We introduce a first-order meta-learning algorithm to train a semantic parser with maximal sample efficiency during cross-lingual transfer.
no code implementations • 1 Jul 2022 • Shashi Narayan, Joshua Maynez, Reinald Kim Amplayo, Kuzman Ganchev, Annie Louis, Fantine Huot, Dipanjan Das, Mirella Lapata
The ability to convey relevant and faithful information is critical for many tasks in conditional generation and yet remains elusive for neural seq-to-seq models whose outputs often reveal hallucinations and fail to correctly cover important details.
1 code implementation • 3 Jun 2022 • Yao Fu, Mirella Lapata
With the induced network, we: (1).
1 code implementation • ACL 2022 • Shashi Narayan, Gonçalo Simões, Yao Zhao, Joshua Maynez, Dipanjan Das, Michael Collins, Mirella Lapata
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.
1 code implementation • ACL 2022 • Tom Hosking, Hao Tang, Mirella Lapata
We propose a generative model of paraphrase generation, that encourages syntactic diversity by conditioning on an explicit syntactic sketch.
Ranked #1 on
Paraphrase Generation
on MSCOCO
1 code implementation • 28 Feb 2022 • Ratish Puduppully, Yao Fu, Mirella Lapata
We consider the task of data-to-text generation, which aims to create textual output from non-linguistic input.
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.
1 code implementation • 7 Dec 2021 • Yao Fu, John P. Cunningham, Mirella Lapata
Here, we propose a family of randomized dynamic programming (RDP) algorithms for scaling structured models to tens of thousands of latent states.
no code implementations • 16 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.
1 code implementation • ACL 2022 • Hao Zheng, Mirella Lapata
There is mounting evidence that existing neural network models, in particular the very popular sequence-to-sequence architecture, struggle to systematically generalize to unseen compositions of seen components.
no code implementations • 29 Sep 2021 • Yao Fu, Mirella Lapata
We use RDP to analyze the representation space of pretrained language models, discovering a large-scale latent network in a fully unsupervised way.
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.
1 code implementation • 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.
no code implementations • 7 Sep 2021 • Parag Jain, Mirella Lapata
We present a memory-based model for context-dependent semantic parsing.
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.
Ranked #3 on
Multi-Document Summarization
on Multi-News
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.
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).
no code implementations • 31 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.
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.
Ranked #2 on
Paraphrase Generation
on Quora Question Pairs
1 code implementation • ACL 2022 • Tom Sherborne, Mirella Lapata
Recent work in cross-lingual semantic parsing has successfully applied machine translation to localize parsers to new languages.
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.
1 code implementation • 4 Feb 2021 • Ratish Puduppully, Mirella Lapata
Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof.
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.
1 code implementation • 14 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
1 code implementation • 14 Dec 2020 • Pinelopi Papalampidi, Frank Keller, Mirella Lapata
We summarize full-length movies by creating shorter videos containing their most informative scenes.
2 code implementations • 8 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.
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.
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.
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.
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.
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.
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.
1 code implementation • 24 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.
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.
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.
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.
no code implementations • 6 Apr 2020 • Yumo Xu, Mirella Lapata
We consider the problem of better modeling query-cluster interactions to facilitate query focused multi-document summarization (QFS).
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.
3 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.
1 code implementation • WS 2019 • Ratish Puduppully, Jonathan Mallinson, Mirella Lapata
The University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with both English and German as targeted languages.
no code implementations • IJCNLP 2019 • Nikos Papasarantopoulos, Lea Frermann, Mirella Lapata, Shay B. Cohen
Multi-view learning algorithms are powerful representation learning tools, often exploited in the context of multimodal problems.
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.
no code implementations • 10 Oct 2019 • Jonathan Mallinson, Mirella Lapata
Sentence simplification aims to make sentences easier to read and understand.
no code implementations • IJCNLP 2019 • Federico Fancellu, Sorcha Gilroy, Adam Lopez, Mirella Lapata
Semantic parses are directed acyclic graphs (DAGs), so semantic parsing should be modeled as graph prediction.
Ranked #5 on
DRS Parsing
on PMB-2.2.0
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.
Ranked #6 on
Semantic Parsing
on WikiTableQuestions
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).
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).
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.
17 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).
1 code implementation • TACL 2019 • Yumo Xu, Mirella Lapata
In this paper we introduce domain detection as a new natural language processing task.
1 code implementation • 19 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?''.
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).
1 code implementation • ACL 2019 • Laura Perez-Beltrachini, Yang Liu, Mirella Lapata
Existing neural generation approaches create multi-sentence text as a single sequence.
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.
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.
Ranked #3 on
Data-to-Text Generation
on MLB Dataset
1 code implementation • NAACL 2019 • Yang Liu, Ivan Titov, Mirella Lapata
In this paper, we conceptualize single-document extractive summarization as a tree induction problem.
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.
no code implementations • WS 2019 • Jiangming Liu, Shay B. Cohen, Mirella Lapata
Our best system achieves a score of 84. 8{\%} F1 in the DRS parsing shared task.
Ranked #2 on
DRS Parsing
on PMB-2.2.0
3 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.
Ranked #6 on
KG-to-Text Generation
on AGENDA
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.
no code implementations • 23 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.
no code implementations • 25 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.
1 code implementation • EMNLP 2018 • Jonathan Mallinson, Rico Sennrich, Mirella Lapata
In this paper we advocate the use of bilingual corpora which are abundantly available for training sentence compression models.
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.
2 code implementations • 3 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.
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.
Ranked #9 on
Text Summarization
on X-Sum
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).
no code implementations • CONLL 2018 • Jianpeng Cheng, Mirella Lapata
Weakly-supervised semantic parsers are trained on utterance-denotation pairs, treating logical forms as latent.
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.
Ranked #11 on
Extractive Text Summarization
on CNN / Daily Mail
1 code implementation • ACL 2018 • Shashi Narayan, Ronald Cardenas, Nikos Papasarantopoulos, Shay B. Cohen, Mirella Lapata, Jiangsheng Yu, Yi Chang
Document modeling is essential to a variety of natural language understanding tasks.
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).
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.
2 code implementations • ACL 2018 • Li Dong, Mirella Lapata
Semantic parsing aims at mapping natural language utterances into structured meaning representations.
Ranked #1 on
Semantic Parsing
on Geo
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.
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.
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.
Ranked #13 on
Extractive Text Summarization
on CNN / Daily Mail
2 code implementations • TACL 2018 • Stefanos Angelidis, Mirella Lapata
We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL).
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.
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.
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.
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.
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.
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.
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.
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.
1 code implementation • 14 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.
no code implementations • EACL 2017 • Jonathan Mallinson, Rico Sennrich, Mirella Lapata
Recognizing and generating paraphrases is an important component in many natural language processing applications.
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.
1 code implementation • EMNLP 2017 • Xingxing Zhang, Mirella Lapata
Sentence simplification aims to make sentences easier to read and understand.
Ranked #2 on
Text Simplification
on ASSET
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.
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.
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.
Ranked #3 on
Chinese Semantic Role Labeling
on CoNLL-2009
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.
2 code implementations • ACL 2016 • Jianpeng Cheng, Mirella Lapata
Traditional approaches to extractive summarization rely heavily on human-engineered features.
5 code implementations • EMNLP 2016 • Jianpeng Cheng, Li Dong, Mirella Lapata
In this paper we address the question of how to render sequence-level networks better at handling structured input.
Ranked #56 on
Natural Language Inference
on SNLI
5 code implementations • ACL 2016 • Li Dong, Mirella Lapata
Semantic parsing aims at mapping natural language to machine interpretable meaning representations.
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.
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.
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.
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.
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.
no code implementations • 15 Jan 2014 • Trevor Anthony Cohn, Mirella Lapata
This paper presents a tree-to-tree transduction method for sentence compression.
no code implementations • 15 Jan 2014 • Sebastian Pado, Mirella Lapata
This article considers the task of automatically inducing role-semantic annotations in the FrameNet paradigm for new languages.
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.