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Found 75 papers, 26 papers with code
Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data
Recent work in learning vector-space embeddings for multi-relational data has focused on combining relational information derived from knowledge bases with distributional information derived from large text corpora.
Predicting sentential semantic compatibility for aggregation in text-to-text generation
We examine the task of aggregation in the context of text-to-text generation.
Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs
We examine the potential of recurrent neural networks for handling pragmatic inferences involving complex contextual cues for the task of article usage prediction.
Predicting Success in Goal-Driven Human-Human Dialogues
We then propose a turn-based hierarchical neural network model that can be used to predict success without requiring a structured goal definition.
World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions
Humans interpret texts with respect to some background information, or world knowledge, and we would like to develop automatic reading comprehension systems that can do the same.
Commonsense mining as knowledge base completion? A study on the impact of novelty
Commonsense knowledge bases such as ConceptNet represent knowledge in the form of relational triples.
Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization
This work provides insight and motivating results for a new general approach to solving coreference and clustering problems with representation learning.
Coarse Lexical Frame Acquisition at the Syntax--Semantics Interface Using a Latent-Variable PCFG Model
We present a method for unsupervised lexical frame acquisition at the syntax{--}semantics interface.
A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge
We introduce an automatic system that performs well on two common-sense reasoning tasks, the Winograd Schema Challenge (WSC) and the Choice of Plausible Alternatives (COPA).
Let's do it "again": A First Computational Approach to Detecting Adverbial Presupposition Triggers
We introduce the task of predicting adverbial presupposition triggers such as also and again.
Let's do it ``again'': A First Computational Approach to Detecting Adverbial Presupposition Triggers
We introduce the novel task of predicting adverbial presupposition triggers, which is useful for natural language generation tasks such as summarization and dialogue systems.
BanditSum: Extractive Summarization as a Contextual Bandit
In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels.
Ranked #10 on
Extractive Text Summarization
on CNN / Daily Mail
A Hierarchical Neural Attention-based Text Classifier
Deep neural networks have been displaying superior performance over traditional supervised classifiers in text classification.
A Knowledge Hunting Framework for Common Sense Reasoning
We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms of inference and knowledge.
Ranked #65 on
Coreference Resolution
on Winograd Schema Challenge
The Knowref Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution
To explain this performance gap, we show empirically that state-of-the art models often fail to capture context, instead relying on the gender or number of candidate antecedents to make a decision.
How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the Winograd Schema Challenge and SWAG
Recent studies have significantly improved the state-of-the-art on common-sense reasoning (CSR) benchmarks like the Winograd Schema Challenge (WSC) and SWAG.
Ranked #36 on
Coreference Resolution
on Winograd Schema Challenge
Contextualized Non-local Neural Networks for Sequence Learning
Recently, a large number of neural mechanisms and models have been proposed for sequence learning, of which self-attention, as exemplified by the Transformer model, and graph neural networks (GNNs) have attracted much attention.
Multi-task Learning over Graph Structures
We present two architectures for multi-task learning with neural sequence models.
Clustering-Oriented Representation Learning with Attractive-Repulsive Loss
The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the training data; building useful representations is not a necessary byproduct of this objective.
On Variational Learning of Controllable Representations for Text without Supervision
The variational autoencoder (VAE) can learn the manifold of natural images on certain datasets, as evidenced by meaningful interpolating or extrapolating in the continuous latent space.
A Cross-Domain Transferable Neural Coherence Model
Coherence is an important aspect of text quality and is crucial for ensuring its readability.
Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples
Neural abstractive summarizers generate summary texts using a language model conditioned on the input source text, and have recently achieved high ROUGE scores on benchmark summarization datasets.
EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing
We present the first sentence simplification model that learns explicit edit operations (ADD, DELETE, and KEEP) via a neural programmer-interpreter approach.
Ranked #2 on
Text Simplification
on PWKP / WikiSmall
(SARI metric)
Referring Expression Generation Using Entity Profiles
Referring Expression Generation (REG) is the task of generating contextually appropriate references to entities.
Countering the Effects of Lead Bias in News Summarization via Multi-Stage Training and Auxiliary Losses
Sentence position is a strong feature for news summarization, since the lead often (but not always) summarizes the key points of the article.
On Posterior Collapse and Encoder Feature Dispersion in Sequence VAEs
Variational autoencoders (VAEs) hold great potential for modelling text, as they could in theory separate high-level semantic and syntactic properties from local regularities of natural language.
Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text
Previous work has focused specifically on modeling physical plausibility and shown that distributional methods fail when tested in a supervised setting.
Deconstructing and reconstructing word embedding algorithms
Uncontextualized word embeddings are reliable feature representations of words used to obtain high quality results for various NLP applications.
Multi-Fact Correction in Abstractive Text Summarization
Pre-trained neural abstractive summarization systems have dominated extractive strategies on news summarization performance, at least in terms of ROUGE.
TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion
Our analysis also reveals important sources of variability both within and across TKG datasets, and we introduce several simple but strong baselines that outperform the prior state of the art in certain settings.
Factual Error Correction for Abstractive Summarization Models
Experimental results show that our model is able to correct factual errors in summaries generated by other neural summarization models and outperforms previous models on factual consistency evaluation on the CNN/DailyMail dataset.
Learning Efficient Task-Specific Meta-Embeddings with Word Prisms
Word embeddings are trained to predict word cooccurrence statistics, which leads them to possess different lexical properties (syntactic, semantic, etc.)
An Analysis of Dataset Overlap on Winograd-Style Tasks
The Winograd Schema Challenge (WSC) and variants inspired by it have become important benchmarks for common-sense reasoning (CSR).
Deconstructing word embedding algorithms
Word embeddings are reliable feature representations of words used to obtain high quality results for various NLP applications.
On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT
In particular, we demonstrate through a simple consistency probe that the ability to correctly retrieve hypernyms in cloze tasks, as used in prior work, does not correspond to systematic knowledge in BERT.
Optimizing Deeper Transformers on Small Datasets
This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of very deep transformers can carry over to challenging tasks with small datasets, including Text-to-SQL semantic parsing and logical reading comprehension.
On-the-Fly Attention Modulation for Neural Generation
Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from degeneration: the generated text is repetitive, generic, self-contradictory, and often lacks commonsense.
Discourse-Aware Unsupervised Summarization for Long Scientific Documents
We propose an unsupervised graph-based ranking model for extractive summarization of long scientific documents.
The Topic Confusion Task: A Novel Scenario for Authorship Attribution
Authorship attribution is the problem of identifying the most plausible author of an anonymous text from a set of candidate authors.
Characterizing Idioms: Conventionality and Contingency
Second, the non-canonical meanings of words in an idiom are contingent on the presence of other words in the idiom.
TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion
The model has to adapt to changes in the TKG for efficient training and inference while preserving its performance on historical knowledge.
Modeling Event Plausibility with Consistent Conceptual Abstraction
Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events.
ADEPT: An Adjective-Dependent Plausibility Task
A false contract is more likely to be rejected than a contract is, yet a false key is less likely than a key to open doors.
Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization
State-of-the-art abstractive summarization systems often generate \emph{hallucinations}; i. e., content that is not directly inferable from the source text.
Abstractive Text Summarization
Reinforcement Learning (RL)
+1
Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge
Transformer models pre-trained with a masked-language-modeling objective (e. g., BERT) encode commonsense knowledge as evidenced by behavioral probes; however, the extent to which this knowledge is acquired by systematic inference over the semantics of the pre-training corpora is an open question.
Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation
Current language generation models suffer from issues such as repetition, incoherence, and hallucinations.
MaskEval: Weighted MLM-Based Evaluation for Text Summarization and Simplification
In text summarization and simplification, system outputs must be evaluated along multiple dimensions such as relevance, factual consistency, fluency, and grammaticality, and a wide range of possible outputs could be of high quality.
The KITMUS Test: Evaluating Knowledge Integration from Multiple Sources in Natural Language Understanding Systems
In this work, we propose a test suite of coreference resolution subtasks that require reasoning over multiple facts.
Learning with Rejection for Abstractive Text Summarization
State-of-the-art abstractive summarization systems frequently hallucinate content that is not supported by the source document, mainly due to noise in the training dataset.
Systematic Rectification of Language Models via Dead-end Analysis
Other methods rely on rule-based or prompt-based token elimination, which are limited as they dismiss future tokens and the overall meaning of the complete discourse.
Investigating Failures to Generalize for Coreference Resolution Models
We investigate the extent to which errors of current coreference resolution models are associated with existing differences in operationalization across datasets (OntoNotes, PreCo, and Winogrande).
Vārta: A Large-Scale Headline-Generation Dataset for Indic Languages
We present V\=arta, a large-scale multilingual dataset for headline generation in Indic languages.
Successor Features for Efficient Multisubject Controlled Text Generation
While large language models (LLMs) have achieved impressive performance in generating fluent and realistic text, controlling the generated text so that it exhibits properties such as safety, factuality, and non-toxicity remains challenging.
Responsible AI Considerations in Text Summarization Research: A Review of Current Practices
We focus on how, which, and when responsible AI issues are covered, which relevant stakeholders are considered, and mismatches between stated and realized research goals.
Identifying and Analyzing Task-Encoding Tokens in Large Language Models
In-context learning (ICL) has become an effective solution for few-shot learning in natural language processing.
$\texttt{COSMIC}$: Mutual Information for Task-Agnostic Summarization Evaluation
Assessing the quality of summarizers poses significant challenges.
Mechanisms of non-factual hallucinations in language models
Our study investigates the mechanistic causes of hallucination, specifically non-factual ones where the LM incorrectly predicts object attributes in response to subject-relation queries.
A Controlled Reevaluation of Coreference Resolution Models
When controlling for language model size, encoder-based CR models outperform more recent decoder-based models in terms of both accuracy and inference speed.
Source-summary Entity Aggregation in Abstractive Summarization
In a text, entities mentioned earlier can be referred to in later discourse by a more general description.
Textual Time Travel: A Temporally Informed Approach to Theory of Mind
This capability, called theory of mind (ToM), is crucial, as it allows a model to predict and interpret the needs of users based on their mental states.
Post-Editing Extractive Summaries by Definiteness Prediction
Moreover, based on an automatic evaluation study, we provide evidence for our system’s ability to generate linguistic decisions that lead to improved extractive summaries.
TESA: A Task in Entity Semantic Aggregation for Abstractive Summarization
Human-written texts contain frequent generalizations and semantic aggregation of content.
The Topic Confusion Task: A Novel Evaluation Scenario for Authorship Attribution
Authorship attribution is the problem of identifying the most plausible author of an anonymous text from a set of candidate authors.
Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences
Presuppositions are assumptions that are taken for granted by an utterance, and identifying them is key to a pragmatic interpretation of language.
