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Found 138 papers, 69 papers with code
Zero-Shot Crosslingual Sentence Simplification
Sentence simplification aims to make sentences easier to read and understand.
Universal Discourse Representation Structure Parsing
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.
Coarse-to-Fine Query Focused Multi-Document Summarization
We consider the problem of better modeling query-cluster interactions to facilitate query focused multi-document summarization.
Alignment-free Cross-lingual Semantic Role Labeling
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.
Semantic Parsing for Conversational Question Answering over Knowledge Graphs
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).
Little Red Riding Hood Goes Around the Globe:Crosslingual Story Planning and Generation with Large Language Models
We consider the problem of automatically generating stories in multiple languages.
mFACE: Multilingual Summarization with Factual Consistency Evaluation
Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets.
Real-World Compositional Generalization with Disentangled Sequence-to-Sequence Learning
Compositional generalization is a basic mechanism in human language learning, which current neural networks struggle with.
QAmeleon: Multilingual QA with Only 5 Examples
The availability of large, high-quality datasets has been one of the main drivers of recent progress in question answering (QA).
Hierarchical3D Adapters for Long Video-to-text Summarization
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).
Explainable Abuse Detection as Intent Classification and Slot Filling
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.
Text Summarization with Oracle Expectation
Extractive summarization produces summaries by identifying and concatenating the most important sentences in a document.
Meta-Learning a Cross-lingual Manifold for Semantic Parsing
We introduce a first-order meta-learning algorithm to train a semantic parser with maximal sample efficiency during cross-lingual transfer.
Conditional Generation with a Question-Answering Blueprint
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.
Latent Topology Induction for Understanding Contextualized Representations
With the induced network, we: (1).
A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation
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.
Hierarchical Sketch Induction for Paraphrase Generation
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
Data-to-text Generation with Variational Sequential Planning
We consider the task of data-to-text generation, which aims to create textual output from non-linguistic input.
Models and Datasets for Cross-Lingual Summarisation
We present a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in a target language.
Scaling Structured Inference with Randomization
Here, 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
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
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.
Discovering Latent Network Topology in Contextualized Representations with Randomized Dynamic Programming
We use RDP to analyze the representation space of pretrained language models, discovering a large-scale latent network in a fully unsupervised way.
Learning Opinion Summarizers by Selecting Informative Reviews
Opinion summarization has been traditionally approached with unsupervised, weakly-supervised and few-shot learning techniques.
Aspect-Controllable Opinion Summarization
Recent work on opinion summarization produces general summaries based on a set of input reviews and the popularity of opinions expressed in them.
Memory-Based Semantic Parsing
We present a memory-based model for context-dependent semantic parsing.
Multi-Document Summarization withDeterminantal Point Process Attention
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
Structured Reordering for Modeling Latent Alignments in Sequence Transduction
Despite success in many domains, neural models struggle in settings where train and test examples are drawn from different distributions.
Text Generation from Discourse Representation Structures
We propose neural models to generate text from formal meaning representations based on Discourse Representation Structures (DRSs).
Text Summarization with Latent Queries
The availability of large-scale datasets has driven the development of neural models that create summaries from single documents, for generic purposes.
Factorising Meaning and Form for Intent-Preserving Paraphrasing
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
Zero-Shot Cross-lingual Semantic Parsing
Recent work in cross-lingual semantic parsing has successfully applied machine translation to localize parsers to new languages.
Learning from Executions for Semantic Parsing
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.
Data-to-text Generation with Macro Planning
Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof.
Generating Query Focused Summaries from Query-Free Resources
The availability of large-scale datasets has driven the development of neural models that create generic summaries from single or multiple documents.
Unsupervised Opinion Summarization with Content Planning
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
Movie Summarization via Sparse Graph Construction
We summarize full-length movies by creating shorter videos containing their most informative scenes.
Extractive Opinion Summarization in Quantized Transformer Spaces
We present the Quantized Transformer (QT), an unsupervised system for extractive opinion summarization.
Compositional Generalization via Semantic Tagging
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.
Meta-Learning for Domain Generalization in Semantic Parsing
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.
Noisy Self-Knowledge Distillation for Text Summarization
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.
Dscorer: A Fast Evaluation Metric for Discourse Representation Structure Parsing
Discourse representation structures (DRSs) are scoped semantic representations for texts of arbitrary length.
Few-Shot Learning for Opinion Summarization
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.
Screenplay Summarization Using Latent Narrative Structure
Most general-purpose extractive summarization models are trained on news articles, which are short and present all important information upfront.
Exploring Explainable Selection to Control Abstractive Summarization
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.
Experience Grounds Language
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.
Unsupervised Opinion Summarization with Noising and Denoising
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.
Multi-Step Inference for Reasoning Over Paragraphs
Complex reasoning over text requires understanding and chaining together free-form predicates and logical connectives.
Query Focused Multi-Document Summarization with Distant Supervision
We consider the problem of better modeling query-cluster interactions to facilitate query focused multi-document summarization (QFS).
Bootstrapping a Crosslingual Semantic Parser
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.
Unsupervised Opinion Summarization as Copycat-Review Generation
At test time, when generating summaries, we force the novelty to be minimal, and produce a text reflecting consensus opinions.
University of Edinburgh's submission to the Document-level Generation and Translation Shared Task
The University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with both English and German as targeted languages.
Partners in Crime: Multi-view Sequential Inference for Movie Understanding
Multi-view learning algorithms are powerful representation learning tools, often exploited in the context of multimodal problems.
Semi-Supervised Semantic Role Labeling with Cross-View Training
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.
Controllable Sentence Simplification: Employing Syntactic and Lexical Constraints
Sentence simplification aims to make sentences easier to read and understand.
Semantic Graph Parsing with Recurrent Neural Network DAG Grammars
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
Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs
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
Informative and Controllable Opinion Summarization
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
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).
Multi-view Story Characterization from Movie Plot Synopses and Reviews
This paper considers the problem of characterizing stories by inferring properties such as theme and style using written synopses and reviews of movies.
Text Summarization with Pretrained Encoders
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).
Weakly Supervised Domain Detection
In this paper we introduce domain detection as a new natural language processing task.
What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks
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?''.
Discourse Representation Parsing for Sentences and Documents
We introduce a novel semantic parsing task based on Discourse Representation Theory (DRT; Kamp and Reyle 1993).
Generating Summaries with Topic Templates and Structured Convolutional Decoders
Existing neural generation approaches create multi-sentence text as a single sequence.
Sentence Centrality Revisited for Unsupervised Summarization
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.
Data-to-text Generation with Entity Modeling
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
Single Document Summarization as Tree Induction
In this paper, we conceptualize single-document extractive summarization as a tree induction problem.
Hierarchical Transformers for Multi-Document Summarization
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.
Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model
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
Text Generation from Knowledge Graphs with Graph Transformers
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
Syntax-aware Semantic Role Labeling without Parsing
In this paper we focus on learning dependency aware representations for semantic role labeling without recourse to an external parser.
Categorization in the Wild: Generalizing Cognitive Models to Naturalistic Data across Languages
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
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.
Sentence Compression for Arbitrary Languages via Multilingual Pivoting
In this paper we advocate the use of bilingual corpora which are abundantly available for training sentence compression models.
Structured Alignment Networks for Matching Sentences
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.
Data-to-Text Generation with Content Selection and Planning
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.
Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
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
Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised
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).
Weakly-supervised Neural Semantic Parsing with a Generative Ranker
Weakly-supervised semantic parsers are trained on utterance-denotation pairs, treating logical forms as latent.
Neural Latent Extractive Document Summarization
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
Document Modeling with External Attention for Sentence Extraction
Document modeling is essential to a variety of natural language understanding tasks.
Discourse Representation Structure Parsing
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).
What's This Movie About? A Joint Neural Network Architecture for Movie Content Analysis
This work takes a first step toward movie content analysis by tackling the novel task of movie overview generation.
Coarse-to-Fine Decoding for Neural Semantic Parsing
Semantic parsing aims at mapping natural language utterances into structured meaning representations.
Ranked #1 on
Semantic Parsing
on Geo
Confidence Modeling for Neural Semantic Parsing
In this work we focus on confidence modeling for neural semantic parsers which are built upon sequence-to-sequence models.
Bootstrapping Generators from Noisy Data
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.
Ranking Sentences for Extractive Summarization with Reinforcement Learning
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
Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis
We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL).
Learning an Executable Neural Semantic Parser
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
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.
Learning Contextually Informed Representations for Linear-Time Discourse Parsing
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.
Learning to Paraphrase for Question Answering
Question answering (QA) systems are sensitive to the many different ways natural language expresses the same information need.
A Generative Parser with a Discriminative Recognition Algorithm
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.
Image Pivoting for Learning Multilingual Multimodal Representations
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.
Learning Structured Text Representations
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
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.
Neural Extractive Summarization with Side Information
Most extractive summarization methods focus on the main body of the document from which sentences need to be extracted.
Paraphrasing Revisited with Neural Machine Translation
Recognizing and generating paraphrases is an important component in many natural language processing applications.
Learning to Generate Product Reviews from Attributes
This paper presents an attention-enhanced attribute-to-sequence model to generate product reviews for given attribute information, such as user, product, and rating.
Sentence Simplification with Deep Reinforcement Learning
Sentence simplification aims to make sentences easier to read and understand.
Ranked #2 on
Text Simplification
on ASSET
Universal Semantic Parsing
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.
Dependency Parsing as Head Selection
Conventional graph-based dependency parsers guarantee a tree structure both during training and inference.
Neural Semantic Role Labeling with Dependency Path Embeddings
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
Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings
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.
Neural Summarization by Extracting Sentences and Words
Traditional approaches to extractive summarization rely heavily on human-engineered features.
Long Short-Term Memory-Networks for Machine Reading
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
Language to Logical Form with Neural Attention
Semantic parsing aims at mapping natural language to machine interpretable meaning representations.
Transforming Dependency Structures to Logical Forms for Semantic Parsing
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.
A Bayesian Model of Diachronic Meaning Change
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.
Top-down Tree Long Short-Term Memory Networks
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.
Which Step Do I Take First? Troubleshooting with Bayesian Models
Online discussion forums and community question-answering websites provide one of the primary avenues for online users to share information.
Context-aware Frame-Semantic Role Labeling
Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis.
Sentence Compression as Tree Transduction
This paper presents a tree-to-tree transduction method for sentence compression.
Cross-lingual Annotation Projection for Semantic Roles
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
In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs.
