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Found 128 papers, 56 papers with code
QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines
Discourse relations describe how two propositions relate to one another, and identifying them automatically is an integral part of natural language understanding.
Design Choices in Crowdsourcing Discourse Relation Annotations: The Effect of Worker Selection and Training
The current contribution studies the effect of worker selection and training on the agreement on implicit relation labels between workers and gold labels, for both the DC and the QA method.
Interactive Query-Assisted Summarization via Deep Reinforcement Learning
Interactive summarization is a task that facilitates user-guided exploration of information within a document set.
Within-Between Lexical Relation Classification
We propose the novel \textit{Within-Between} Relation model for recognizing lexical-semantic relations between words.
Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction
In this paper, we investigate and establish empirically a prior conjecture, which suggests that the linguistic relations connecting opinion terms to their aspects transfer well across domains and therefore can be leveraged for cross-domain aspect term extraction.
Explicating the Implicit: Argument Detection Beyond Sentence Boundaries
Detecting semantic arguments of a predicate word has been conventionally modeled as a sentence-level task.
Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP
By using a descriptive vocabulary and discussing the relevant properties of difficulty in long-context, we can implement more informed research in this area.
Identifying User Goals from UI Trajectories
By effectively comprehending user intentions through their actions and interactions with GUIs, agents will be better positioned to achieve these goals.
The Power of Summary-Source Alignments
Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection, followed by text generation.
Superlatives in Context: Explicit and Implicit Domain Restrictions for Superlative Frames
Semantically, superlatives perform a set comparison: something (or some things) has the min/max property out of a set.
Selective Annotation via Data Allocation: These Data Should Be Triaged to Experts for Annotation Rather Than the Model
As such, informative or hard data is assigned to the expert for annotation, while easy data is handled by the model.
Efficient Data Generation for Source-grounded Information-seeking Dialogs: A Use Case for Meeting Transcripts
Automating data generation with Large Language Models (LLMs) has become increasingly popular.
Attribute First, then Generate: Locally-attributable Grounded Text Generation
Recent efforts to address hallucinations in Large Language Models (LLMs) have focused on attributed text generation, which supplements generated texts with citations of supporting sources for post-generation fact-checking and corrections.
Multi-Review Fusion-in-Context
This study lays the groundwork for further exploration of modular text generation in the multi-document setting, offering potential improvements in the quality and reliability of generated content.
OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization
To advance research on more realistic scenarios, we introduce OpenAsp, a benchmark for multi-document \textit{open} aspect-based summarization.
CHAMP: Efficient Annotation and Consolidation of Cluster Hierarchies
Various NLP tasks require a complex hierarchical structure over nodes, where each node is a cluster of items.
coreference-resolution
Cross Document Coreference Resolution
Optimizing Retrieval-augmented Reader Models via Token Elimination
Fusion-in-Decoder (FiD) is an effective retrieval-augmented language model applied across a variety of open-domain tasks, such as question answering, fact checking, etc.
The Curious Case of Hallucinatory (Un)answerability: Finding Truths in the Hidden States of Over-Confident Large Language Models
In this paper, we explore the behavior of LLMs when presented with (un)answerable queries.
Dont Add, dont Miss: Effective Content Preserving Generation from Pre-Selected Text Spans
Further, we substantially improve the silver training data quality via GPT-4 distillation.
SummHelper: Collaborative Human-Computer Summarization
Current approaches for text summarization are predominantly automatic, with rather limited space for human intervention and control over the process.
Revisiting Sentence Union Generation as a Testbed for Text Consolidation
In this paper, we suggest revisiting the sentence union generation task as an effective well-defined testbed for assessing text consolidation capabilities, decoupling the consolidation challenge from subjective content selection.
Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering
The integration of multi-document pre-training objectives into language models has resulted in remarkable improvements in multi-document downstream tasks.
Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design
Disagreement in natural language annotation has mostly been studied from a perspective of biases introduced by the annotators and the annotation frameworks.
Controlled Text Reduction
Producing a reduced version of a source text, as in generic or focused summarization, inherently involves two distinct subtasks: deciding on targeted content and generating a coherent text conveying it.
Cross-document Event Coreference Search: Task, Dataset and Modeling
The task of Cross-document Coreference Resolution has been traditionally formulated as requiring to identify all coreference links across a given set of documents.
Cross Document Coreference Resolution
Open-Domain Question Answering
+2
How "Multi" is Multi-Document Summarization?
To that end, we propose an automated measure for evaluating the degree to which a summary is ``disperse'', in the sense of the number of source documents needed to cover its content.
QASem Parsing: Text-to-text Modeling of QA-based Semantics
Several recent works have suggested to represent semantic relations with questions and answers, decomposing textual information into separate interrogative natural language statements.
Proposition-Level Clustering for Multi-Document Summarization
Text clustering methods were traditionally incorporated into multi-document summarization (MDS) as a means for coping with considerable information repetition.
Long Context Question Answering via Supervised Contrastive Learning
Long-context question answering (QA) tasks require reasoning over a long document or multiple documents.
Extending Multi-Text Sentence Fusion Resources via Pyramid Annotations
NLP models that compare or consolidate information across multiple documents often struggle when challenged with recognizing substantial information redundancies across the texts.
Multi-Document Keyphrase Extraction: Dataset, Baselines and Review
Keyphrase extraction has been extensively researched within the single-document setting, with an abundance of methods, datasets and applications.
QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions
Multi-text applications, such as multi-document summarization, are typically required to model redundancies across related texts.
iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration
We introduce iFacetSum, a web application for exploring topical document sets.
Asking It All: Generating Contextualized Questions for any Semantic Role
We develop a two-stage model for this task, which first produces a context-independent question prototype for each role and then revises it to be contextually appropriate for the passage.
Teach the Rules, Provide the Facts: Targeted Relational-knowledge Enhancement for Textual Inference
Our approach facilitates learning generic inference patterns requiring relational knowledge (e. g. inferences related to hypernymy) during training, while injecting on-demand the relevant relational facts (e. g. pangolin is an animal) at test time.
Realistic Evaluation Principles for Cross-document Coreference Resolution
We point out that common evaluation practices for cross-document coreference resolution have been unrealistically permissive in their assumed settings, yielding inflated results.
coreference-resolution
Cross Document Coreference Resolution
Denoising Word Embeddings by Averaging in a Shared Space
We introduce a new approach for smoothing and improving the quality of word embeddings.
Cross-document Coreference Resolution over Predicted Mentions
Here, we introduce the first end-to-end model for CD coreference resolution from raw text, which extends the prominent model for within-document coreference to the CD setting.
coreference-resolution
Cross Document Coreference Resolution
Extending Multi-Document Summarization Evaluation to the Interactive Setting
In this paper, we develop an end-to-end evaluation framework for interactive summarization, focusing on expansion-based interaction, which considers the accumulating information along a user session.
SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts
Determining coreference of concept mentions across multiple documents is a fundamental task in natural language understanding.
coreference-resolution
Cross Document Coreference Resolution
+1
Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes
We explore Few-Shot Learning (FSL) for Relation Classification (RC).
WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia
To complement these resources and enhance future research, we present Wikipedia Event Coreference (WEC), an efficient methodology for gathering a large-scale dataset for cross-document event coreference from Wikipedia, where coreference links are not restricted within predefined topics.
CD\^2CR: Co-reference resolution across documents and domains
Cross-document co-reference resolution (CDCR) is the task of identifying and linking mentions to entities and concepts across many text documents.
CD2CR: Co-reference Resolution Across Documents and Domains
Cross-document co-reference resolution (CDCR) is the task of identifying and linking mentions to entities and concepts across many text documents.
CDLM: Cross-Document Language Modeling
We introduce a new pretraining approach geared for multi-document language modeling, incorporating two key ideas into the masked language modeling self-supervised objective.
Ranked #1 on
Citation Recommendation
on AAN test
QANom: Question-Answer driven SRL for Nominalizations
We propose a new semantic scheme for capturing predicate-argument relations for nominalizations, termed QANom.
QADiscourse -- Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines
Discourse relations describe how two propositions relate to one another, and identifying them automatically is an integral part of natural language understanding.
CoRefi: A Crowd Sourcing Suite for Coreference Annotation
Coreference annotation is an important, yet expensive and time consuming, task, which often involved expert annotators trained on complex decision guidelines.
Streamlining Cross-Document Coreference Resolution: Evaluation and Modeling
Recent evaluation protocols for Cross-document (CD) coreference resolution have often been inconsistent or lenient, leading to incomparable results across works and overestimation of performance.
coreference-resolution
Cross Document Coreference Resolution
+2
Evaluating Interactive Summarization: an Expansion-Based Framework
Allowing users to interact with multi-document summarizers is a promising direction towards improving and customizing summary results.
Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline
Aligning sentences in a reference summary with their counterparts in source documents was shown as a useful auxiliary summarization task, notably for generating training data for salience detection.
Paraphrasing vs Coreferring: Two Sides of the Same Coin
We study the potential synergy between two different NLP tasks, both confronting predicate lexical variability: identifying predicate paraphrases, and event coreference resolution.
Controlled Crowdsourcing for High-Quality QA-SRL Annotation
Question-answer driven Semantic Role Labeling (QA-SRL) was proposed as an attractive open and natural flavour of SRL, potentially attainable from laymen.
Diversify Your Datasets: Analyzing Generalization via Controlled Variance in Adversarial Datasets
Phenomenon-specific "adversarial" datasets have been recently designed to perform targeted stress-tests for particular inference types.
Revisiting the Binary Linearization Technique for Surface Realization
End-to-end neural approaches have achieved state-of-the-art performance in many natural language processing (NLP) tasks.
Improving Quality and Efficiency in Plan-based Neural Data-to-Text Generation
We follow the step-by-step approach to neural data-to-text generation we proposed in Moryossef et al (2019), in which the generation process is divided into a text-planning stage followed by a plan-realization stage.
ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System
We present ABSApp, a portable system for weakly-supervised aspect-based sentiment extraction.
Better Rewards Yield Better Summaries: Learning to Summarise Without References
Human evaluation experiments show that, compared to the state-of-the-art supervised-learning systems and ROUGE-as-rewards RL summarisation systems, the RL systems using our learned rewards during training generate summarieswith higher human ratings.
Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference
While recent progress on abstractive summarization has led to remarkably fluent summaries, factual errors in generated summaries still severely limit their use in practice.
Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution
Our analysis confirms that all our representation elements, including the mention span itself, its context, and the relation to other mentions contribute to the model's success.
coreference-resolution
Cross Document Coreference Resolution
+4
How to Compare Summarizers without Target Length? Pitfalls, Solutions and Re-Examination of the Neural Summarization Literature
We show that plain ROUGE F1 scores are not ideal for comparing current neural systems which on average produce different lengths.
Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation
Conducting a manual evaluation is considered an essential part of summary evaluation methodology.
Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation
We propose to split the generation process into a symbolic text-planning stage that is faithful to the input, followed by a neural generation stage that focuses only on realization.
Ranked #15 on
Data-to-Text Generation
on WebNLG
Multi-Context Term Embeddings: the Use Case of Corpus-based Term Set Expansion
In this paper, we present a novel algorithm that combines multi-context term embeddings using a neural classifier and we test this approach on the use case of corpus-based term set expansion.
Still a Pain in the Neck: Evaluating Text Representations on Lexical Composition
Building meaningful phrase representations is challenging because phrase meanings are not simply the sum of their constituent meanings.
Evaluating Multiple System Summary Lengths: A Case Study
Practical summarization systems are expected to produce summaries of varying lengths, per user needs.
Semantics as a Foreign Language
We propose a novel approach to semantic dependency parsing (SDP) by casting the task as an instance of multi-lingual machine translation, where each semantic representation is a different foreign dialect.
SetExpander: End-to-end Term Set Expansion Based on Multi-Context Term Embeddings
We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class.
Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow
We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class.
Supervised Open Information Extraction
We present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE).
Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations
Revealing the implicit semantic relation between the constituents of a noun-compound is important for many NLP applications.
Crowdsourcing Question-Answer Meaning Representations
We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs.
Interactive Abstractive Summarization for Event News Tweets
We present a novel interactive summarization system that is based on abstractive summarization, derived from a recent consolidated knowledge representation for multiple texts.
Acquiring Predicate Paraphrases from News Tweets
We present a simple method for ever-growing extraction of predicate paraphrases from news headlines in Twitter.
A Simple Language Model based on PMI Matrix Approximations
Specifically, we show that with minor modifications to word2vec's algorithm, we get principled language models that are closely related to the well-established Noise Contrastive Estimation (NCE) based language models.
Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets
Previous models for the assessment of commitment towards a predicate in a sentence (also known as factuality prediction) were trained and tested against a specific annotated dataset, subsequently limiting the generality of their results.
A Consolidated Open Knowledge Representation for Multiple Texts
We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open text-based manner.
Modeling Extractive Sentence Intersection via Subtree Entailment
Sentence intersection captures the semantic overlap of two texts, generalizing over paradigms such as textual entailment and semantic text similarity.
Abstractive Text Summarization
Natural Language Inference
+2
CogALex-V Shared Task: LexNET - Integrated Path-based and Distributional Method for the Identification of Semantic Relations
The reported results in the shared task bring this submission to the third place on subtask 1 (word relatedness), and the first place on subtask 2 (semantic relation classification), demonstrating the utility of integrating the complementary path-based and distributional information sources in recognizing concrete semantic relations.
PMI Matrix Approximations with Applications to Neural Language Modeling
The obtained language modeling is closely related to NCE language models but is based on a simplified objective function.
Path-based vs. Distributional Information in Recognizing Lexical Semantic Relations
Recognizing various semantic relations between terms is beneficial for many NLP tasks.
The Negochat Corpus of Human-agent Negotiation Dialogues
In this work, we introduce an annotated natural language human-agent dialogue corpus in the negotiation domain.
Improving Hypernymy Detection with an Integrated Path-based and Distributional Method
Detecting hypernymy relations is a key task in NLP, which is addressed in the literature using two complementary approaches.
Getting More Out Of Syntax with PropS
Semantic NLP applications often rely on dependency trees to recognize major elements of the proposition structure of sentences.
Ranked #27 on
Open Information Extraction
on CaRB
Improving Distributional Similarity with Lessons Learned from Word Embeddings
Recent trends suggest that neural-network-inspired word embedding models outperform traditional count-based distributional models on word similarity and analogy detection tasks.
