Search Results for author:
Found 279 papers, 81 papers with code
An NLP Curator (or: How I Learned to Stop Worrying and Love NLP Pipelines)
Natural Language Processing continues to grow in popularity in a range of research and commercial applications, yet managing the wide array of potential NLP components remains a difficult problem.
Modeling Semantic Relations Expressed by Prepositions
This paper introduces the problem of predicting semantic relations expressed by prepositions and develops statistical learning models for predicting the relations, their arguments and the semantic types of the arguments.
An Inventory of Preposition Relations
We describe an inventory of semantic relations that are expressed by prepositions.
Building a State-of-the-Art Grammatical Error Correction System
This paper identifies and examines the key principles underlying building a state-of-the-art grammatical error correction system.
ILLINOISCLOUDNLP: Text Analytics Services in the Cloud
Natural Language Processing (NLP) continues to grow in popularity in a range of research and commercial applications.
Reasoning about Quantities in Natural Language
In order to address these quantitative reasoning problems we first develop a computational approach which we show to successfully recognize and normalize textual expressions of quantities.
From Paraphrase Database to Compositional Paraphrase Model and Back
The Paraphrase Database (PPDB; Ganitkevitch et al., 2013) is an extensive semantic resource, consisting of a list of phrase pairs with (heuristic) confidence estimates.
IllinoisSL: A JAVA Library for Structured Prediction
IllinoisSL is a Java library for learning structured prediction models.
Concept Grounding to Multiple Knowledge Bases via Indirect Supervision
We also show that considering multiple knowledge bases together has an advantage over grounding concepts to each knowledge base individually.
Cross-lingual Models of Word Embeddings: An Empirical Comparison
Despite interest in using cross-lingual knowledge to learn word embeddings for various tasks, a systematic comparison of the possible approaches is lacking in the literature.
Question Answering via Integer Programming over Semi-Structured Knowledge
We propose a structured inference system for this task, formulated as an Integer Linear Program (ILP), that answers natural language questions using a semi-structured knowledge base derived from text, including questions requiring multi-step inference and a combination of multiple facts.
EDISON: Feature Extraction for NLP, Simplified
We present EDISON, a Java library of feature generation functions used in a suite of state-of-the-art NLP tools, based on a set of generic NLP data structures.
Two Discourse Driven Language Models for Semantics
Natural language understanding often requires deep semantic knowledge.
World Knowledge as Indirect Supervision for Document Clustering
We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network.
Solving General Arithmetic Word Problems
This paper presents a novel approach to automatically solving arithmetic word problems.
Transliteration in Any Language with Surrogate Languages
We introduce a method for transliteration generation that can produce transliterations in every language.
Equation Parsing: Mapping Sentences to Grounded Equations
We introduce the problem of Equation Parsing -- given a sentence, identify noun phrases which represent variables, and generate the mathematical equation expressing the relation described in the sentence.
Cross-lingual Dataless Classification for Languages with Small Wikipedia Presence
We use a word-level dictionary to convert documents in a SWL to a large-Wikipedia language (LWLs), and then perform CLDDC based on the LWL's Wikipedia.
Illinois Cross-Lingual Wikifier: Grounding Entities in Many Languages to the English Wikipedia
The cross-lingual NER model is a language-independent model which can extract named entity mentions in the text of any language in Wikipedia.
Revisiting the Evaluation for Cross Document Event Coreference
Cross document event coreference (CDEC) is an important task that aims at aggregating event-related information across multiple documents.
Better call Saul: Flexible Programming for Learning and Inference in NLP
We present a novel way for designing complex joint inference and learning models using Saul (Kordjamshidi et al., 2015), a recently-introduced declarative learning-based programming language (DeLBP).
Unit Dependency Graph and its Application to Arithmetic Word Problem Solving
Math word problems provide a natural abstraction to a range of natural language understanding problems that involve reasoning about quantities, such as interpreting election results, news about casualties, and the financial section of a newspaper.
Integer Linear Programming formulations in Natural Language Processing
We will cover a range of topics, from the theoretical foundations of learning and inference with ILP models, to practical modeling guides, to software packages and applications. The goal of this tutorial is to introduce the computational framework to broader ACL community, motivate it as a generic framework for learning and inference in global NLP decision problems, present some of the key theoretical and practical issues involved and survey some of the existing applications of it as a way to promote further development of the framework and additional applications.
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.
Machine Learning with World Knowledge: The Position and Survey
In this paper, we will discuss how to use the existing general-purpose world knowledge to enhance machine learning processes, by enriching the features or reducing the labeling work.
Relational Learning and Feature Extraction by Querying over Heterogeneous Information Networks
In particular, we provide an initial prototype for a relational and graph traversal query language where queries are directly used as relational features for structured machine learning models.
Learning What is Essential in Questions
Question answering (QA) systems are easily distracted by irrelevant or redundant words in questions, especially when faced with long or multi-sentence questions in difficult domains.
A Joint Model for Semantic Sequences: Frames, Entities, Sentiments
Understanding stories {--} sequences of events {--} is a crucial yet challenging natural language understanding task.
Towards Problem Solving Agents that Communicate and Learn
Agents that communicate back and forth with humans to help them execute non-linguistic tasks are a long sought goal of AI.
Cheap Translation for Cross-Lingual Named Entity Recognition
Recent work in NLP has attempted to deal with low-resource languages but still assumed a resource level that is not present for most languages, e. g., the availability of Wikipedia in the target language.
Entity Linking via Joint Encoding of Types, Descriptions, and Context
For accurate entity linking, we need to capture various information aspects of an entity, such as its description in a KB, contexts in which it is mentioned, and structured knowledge.
Story Comprehension for Predicting What Happens Next
Automatic story comprehension is a fundamental challenge in Natural Language Understanding, and can enable computers to learn about social norms, human behavior and commonsense.
Ranked #13 on
Question Answering
on StoryCloze
Adapting to Learner Errors with Minimal Supervision
This article considers the problem of correcting errors made by English as a Second Language writers from a machine learning perspective, and addresses an important issue of developing an appropriate training paradigm for the task, one that accounts for error patterns of non-native writers using minimal supervision.
Mapping to Declarative Knowledge for Word Problem Solving
Solving such problems requires the understanding of several mathematical concepts such as dimensional analysis, subset relationships, etc.
Robust Cross-lingual Hypernymy Detection using Dependency Context
We propose BISPARSE-DEP, a family of unsupervised approaches for cross-lingual hypernymy detection, which learns sparse, bilingual word embeddings based on dependency contexts.
Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource
We argue that this task would gain from the availability of a resource that provides prior knowledge in the form of the temporal order that events usually follow.
Exploiting Partially Annotated Data for Temporal Relation Extraction
As a result, only a small number of documents are typically annotated, limiting the coverage of various lexical/semantic phenomena.
Preference-Guided Planning: An Active Elicitation Approach
Planning with preferences has been employed extensively to quickly generate high-quality plans.
A Multi-Axis Annotation Scheme for Event Temporal Relations
Existing temporal relation (TempRel) annotation schemes often have low inter-annotator agreements (IAA) even between experts, suggesting that the current annotation task needs a better definition.
End-Task Oriented Textual Entailment via Deep Explorations of Inter-Sentence Interactions
This work deals with SciTail, a natural entailment challenge derived from a multi-choice question answering problem.
Robust Handling of Polysemy via Sparse Representations
Words are polysemous and multi-faceted, with many shades of meanings.
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences
We present a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences.
Exploiting Partially Annotated Data in Temporal Relation Extraction
As a result, only a small number of documents are typically annotated, limiting the coverage of various lexical/semantic phenomena.
Where Have I Heard This Story Before? Identifying Narrative Similarity in Movie Remakes
People can identify correspondences between narratives in everyday life.
Term Definitions Help Hypernymy Detection
Existing methods of hypernymy detection mainly rely on statistics over a big corpus, either mining some co-occurring patterns like "animals such as cats" or embedding words of interest into context-aware vectors.
TALEN: Tool for Annotation of Low-resource ENtities
We present a new web-based interface, TALEN, designed for named entity annotation in low-resource settings where the annotators do not speak the language.
Multi-lingual Entity Discovery and Linking
We will then proceed to Cross-lingual EL and discuss methods that work across languages.
A Distributional and Orthographic Aggregation Model for English Derivational Morphology
Modeling derivational morphology to generate words with particular semantics is useful in many text generation tasks, such as machine translation or abstractive question answering.
Gold Standard Annotations for Preposition and Verb Sense with Semantic Role Labels in Adult-Child Interactions
Inter-annotator agreement is given separately for prepositions and verbs, and for adult speech and child speech.
TwoWingOS: A Two-Wing Optimization Strategy for Evidential Claim Verification
We develop TwoWingOS (two-wing optimization strategy), a system that, while identifying appropriate evidence for a claim, also determines whether or not the claim is supported by the evidence.
On the Strength of Character Language Models for Multilingual Named Entity Recognition
Character-level patterns have been widely used as features in English Named Entity Recognition (NER) systems.
Multilingual Named Entity Recognition
named-entity-recognition
+2
Bootstrapping Transliteration with Constrained Discovery for Low-Resource Languages
Generating the English transliteration of a name written in a foreign script is an important and challenging step in multilingual knowledge acquisition and information extraction.
Joint Multilingual Supervision for Cross-lingual Entity Linking
This enables our approach to: (a) augment the limited supervision in the target language with additional supervision from a high-resource language (like English), and (b) train a single entity linking model for multiple languages, improving upon individually trained models for each language.
Named Person Coreference in English News
Here, we evaluate two state of the art coreference resolution systems on the subtask of Named Person Coreference, in which we are interested in identifying a person mentioned by name, along with all other mentions of the person, by pronoun or generic noun phrase.
CogCompTime: A Tool for Understanding Time in Natural Language
Automatic extraction of temporal information is important for natural language understanding.
Discourse in Multimedia: A Case Study in Information Extraction
At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information.
Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems
Finally, we also show how Nuts&Bolts can be used to achieve improvements on a relation extraction task and on the end task of answering Newtonian physics problems.
On the Possibilities and Limitations of Multi-hop Reasoning Under Linguistic Imperfections
The idea is to consider two interrelated spaces: a conceptual meaning space that is unambiguous and complete but hidden, and a linguistic space that captures a noisy grounding of the meaning space in the words of a language---the level at which all systems, whether neural or symbolic, operate.
Evidence Sentence Extraction for Machine Reading Comprehension
Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks.
Grammar Error Correction in Morphologically Rich Languages: The Case of Russian
Although impressive results have recently been achieved for grammar error correction of non-native English writing, these results are limited to domains where plentiful training data are available.
ner and pos when nothing is capitalized
While prior work and first impressions might suggest training a caseless model, or using a truecaser at test time, we show that the most effective strategy is a concatenation of cased and lowercased training data, producing a single model with high performance on both cased and uncased text.
Seeing Things from a Different Angle:Discovering Diverse Perspectives about Claims
Inherently, this is a natural language understanding task, and we propose to address it as such.
Evaluation of named entity coreference
It is therefore important that coreference resolution systems are able to link these different types of mentions to the correct entity name.
How Large Are Lions? Inducing Distributions over Quantitative Attributes
Most current NLP systems have little knowledge about quantitative attributes of objects and events.
Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims
Inherently, this is a natural language understanding task, and we propose to address it as such.
Question Answering as Global Reasoning over Semantic Abstractions
We propose a novel method for exploiting the semantic structure of text to answer multiple-choice questions.
PerspectroScope: A Window to the World of Diverse Perspectives
This work presents PerspectroScope, a web-based system which lets users query a discussion-worthy natural language claim, and extract and visualize various perspectives in support or against the claim, along with evidence supporting each perspective.
Natural Language Inference
Natural Language Understanding
+1
CogCompTime: A Tool for Understanding Time in Natural Language Text
Automatic extraction of temporal information in text is an important component of natural language understanding.
Partial Or Complete, That's The Question
For many structured learning tasks, the data annotation process is complex and costly.
A Structured Learning Approach to Temporal Relation Extraction
Identifying temporal relations between events is an essential step towards natural language understanding.
Ranked #1 on
Temporal Information Extraction
on TempEval-3
Joint Reasoning for Temporal and Causal Relations
Understanding temporal and causal relations between events is a fundamental natural language understanding task.
Declarative Learning-Based Programming as an Interface to AI Systems
Data-driven approaches are becoming more common as problem-solving techniques in many areas of research and industry.
Evidence-based Trustworthiness
This paper develops a general framework for estimating the trustworthiness of information sources in an environment where multiple sources provide claims and supporting evidence, and each claim can potentially be produced by multiple sources.
Zero-Shot Open Entity Typing as Type-Compatible Grounding
We evaluate our system on a broad range of datasets, including standard fine-grained and coarse-grained entity typing datasets, and also a dataset in the biological domain.
Solving Hard Coreference Problems
Coreference resolution is a key problem in natural language understanding that still escapes reliable solutions.
BSNLP2019 Shared Task Submission: Multisource Neural NER Transfer
This paper describes the Cognitive Computation (CogComp) Group{'}s submissions to the multilingual named entity recognition shared task at the Balto-Slavic Natural Language Processing (BSNLP) Workshop.
Multilingual Named Entity Recognition
named-entity-recognition
+2
Improving Generalization in Coreference Resolution via Adversarial Training
In order for coreference resolution systems to be useful in practice, they must be able to generalize to new text.
Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach
0Shot-TC aims to associate an appropriate label with a piece of text, irrespective of the text domain and the aspect (e. g., topic, emotion, event, etc.)
An Improved Neural Baseline for Temporal Relation Extraction
Determining temporal relations (e. g., before or after) between events has been a challenging natural language understanding task, partly due to the difficulty to generate large amounts of high-quality training data.
QuASE: Question-Answer Driven Sentence Encoding
Question-answering (QA) data often encodes essential information in many facets.
"Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding
Understanding time is crucial for understanding events expressed in natural language.
Named Entity Recognition with Partially Annotated Training Data
Supervised machine learning assumes the availability of fully-labeled data, but in many cases, such as low-resource languages, the only data available is partially annotated.
``Going on a vacation'' takes longer than ``Going for a walk'': A Study of Temporal Commonsense Understanding
Understanding time is crucial for understanding events expressed in natural language.
KnowSemLM: A Knowledge Infused Semantic Language Model
Story understanding requires developing expectations of what events come next in text.
A General-Purpose Algorithm for Constrained Sequential Inference
We experimentally show the benefits of our algorithm on constituency parsing and semantic role labeling.
Summary Cloze: A New Task for Content Selection in Topic-Focused Summarization
A key challenge in topic-focused summarization is determining what information should be included in the summary, a problem known as content selection.
Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess Hypotheses
Empirical research in Natural Language Processing (NLP) has adopted a narrow set of principles for assessing hypotheses, relying mainly on p-value computation, which suffers from several known issues.
Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks
At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information.
Neural Module Networks for Reasoning over Text
Answering compositional questions that require multiple steps of reasoning against text is challenging, especially when they involve discrete, symbolic operations.
Robust Named Entity Recognition with Truecasing Pretraining
Although modern named entity recognition (NER) systems show impressive performance on standard datasets, they perform poorly when presented with noisy data.
Ranked #10 on
Named Entity Recognition (NER)
on WNUT 2017
Cross-Lingual Ability of Multilingual BERT: An Empirical Study
Recent work has exhibited the surprising cross-lingual abilities of multilingual BERT (M-BERT) -- surprising since it is trained without any cross-lingual objective and with no aligned data.
Extending Multilingual BERT to Low-Resource Languages
Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning.
TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions
A critical part of reading is being able to understand the temporal relationships between events described in a passage of text, even when those relationships are not explicitly stated.
Ranked #2 on
Question Answering
on Torque
Cross-lingual Entity Alignment with Incidental Supervision
Much research effort has been put to multilingual knowledge graph (KG) embedding methods to address the entity alignment task, which seeks to match entities in different languagespecific KGs that refer to the same real-world object.
Ranked #19 on
Entity Alignment
on DBP15k zh-en
TransOMCS: From Linguistic Graphs to Commonsense Knowledge
Commonsense knowledge acquisition is a key problem for artificial intelligence.
Design Challenges in Low-resource Cross-lingual Entity Linking
Cross-lingual Entity Linking (XEL), the problem of grounding mentions of entities in a foreign language text into an English knowledge base such as Wikipedia, has seen a lot of research in recent years, with a range of promising techniques.
Temporal Common Sense Acquisition with Minimal Supervision
Temporal common sense (e. g., duration and frequency of events) is crucial for understanding natural language.
Context-Based Quotation Recommendation
Our experiments confirm the strong performance of BERT-based methods on this task, which outperform bag-of-words and neural ranking baselines by more than 30% relative across all ranking metrics.
Text Classification with Few Examples using Controlled Generalization
Training data for text classification is often limited in practice, especially for applications with many output classes or involving many related classification problems.
Incidental Supervision: Moving beyond Supervised Learning
Machine Learning and Inference methods have become ubiquitous in our attempt to induce more abstract representations of natural language text, visual scenes, and other messy, naturally occurring data, and support decisions that depend on it.
Foreseeing the Benefits of Incidental Supervision
Real-world applications often require improved models by leveraging a range of cheap incidental supervision signals.
Learnability with Indirect Supervision Signals
Learning from indirect supervision signals is important in real-world AI applications when, often, gold labels are missing or too costly.
Building Low-Resource NER Models Using Non-Speaker Annotation
In low-resource natural language processing (NLP), the key problems are a lack of target language training data, and a lack of native speakers to create it.
Low Resource Named Entity Recognition
named-entity-recognition
+2
``Who said it, and Why?'' Provenance for Natural Language Claims
In an era where generating content and publishing it is so easy, we are bombarded with information and are exposed to all kinds of claims, some of which do not always rank high on the truth scale.
Commonsense Reasoning for Natural Language Processing
We organize this tutorial to provide researchers with the critical foundations and recent advances in commonsense representation and reasoning, in the hopes of casting a brighter light on this promising area of future research.
SacreROUGE: An Open-Source Library for Using and Developing Summarization Evaluation Metrics
We present SacreROUGE, an open-source library for using and developing summarization evaluation metrics.
Understanding Spatial Relations through Multiple Modalities
Recognizing spatial relations and reasoning about them is essential in multiple applications including navigation, direction giving and human-computer interaction in general.
From Spatial Relations to Spatial Configurations
To exhibit the applicability of our representation scheme, we annotate text taken from diverse datasets and show how we extend the capabilities of existing spatial representation languages with the fine-grained decomposition of semantics and blend it seamlessly with AMRs of sentences and discourse representations as a whole.
Task-Oriented Dialogue as Dataflow Synthesis
We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph.
Visual Pivoting for (Unsupervised) Entity Alignment
This work studies the use of visual semantic representations to align entities in heterogeneous knowledge graphs (KGs).
Ranked #3 on
Multi-modal Entity Alignment
on UMVM-oea-d-w-v1
(using extra training data)
Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary
A desirable property of a reference-based evaluation metric that measures the content quality of a summary is that it should estimate how much information that summary has in common with a reference.
Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior
Traditional (unstructured) pruning methods for a Transformer model focus on regularizing the individual weights by penalizing them toward zero.
"I'd rather just go to bed": Understanding Indirect Answers
We revisit a pragmatic inference problem in dialog: understanding indirect responses to questions.
Do Language Embeddings Capture Scales?
Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense, and factual knowledge.
Joint Constrained Learning for Event-Event Relation Extraction
Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other.
"What Are You Trying to Do?" Semantic Typing of Event Processes
This paper studies a new cognitively motivated semantic typing task, multi-axis event process typing, that, given an event process, attempts to infer free-form type labels describing (i) the type of action made by the process and (ii) the type of object the process seeks to affect.
Analogous Process Structure Induction for Sub-event Sequence Prediction
Computational and cognitive studies of event understanding suggest that identifying, comprehending, and predicting events depend on having structured representations of a sequence of events and on conceptualizing (abstracting) its components into (soft) event categories.
Understanding the Extent to which Summarization Evaluation Metrics Measure the Information Quality of Summaries
Reference-based metrics such as ROUGE or BERTScore evaluate the content quality of a summary by comparing the summary to a reference.
Pairwise Representation Learning for Event Coreference
Natural Language Processing tasks such as resolving the coreference of events require understanding the relations between two text snippets.
Temporal Reasoning on Implicit Events from Distant Supervision
We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events -- events that are not mentioned explicitly in natural language text but can be inferred from it.
What Are You Trying to Do? Semantic Typing of Event Processes
This paper studies a new cognitively motivated semantic typing task, multi-axis event process typing, that, given anevent process, attempts to infer free-form typelabels describing (i) the type of action made bythe process and (ii) the type of object the pro-cess seeks to affect.
What do we expect from Multiple-choice QA Systems?
The recent success of machine learning systems on various QA datasets could be interpreted as a significant improvement in models' language understanding abilities.
Is Killed More Significant than Fled? A Contextual Model for Salient Event Detection
Identifying the key events in a document is critical to holistically understanding its important information.
QANom: Question-Answer driven SRL for Nominalizations
We propose a new semantic scheme for capturing predicate-argument relations for nominalizations, termed QANom.
Learning Contextual Causality from Time-consecutive Images
Compared with pure text-based approaches, learning causality from the visual signal has the following advantages: (1) Causality knowledge belongs to the commonsense knowledge, which is rarely expressed in the text but rich in videos; (2) Most events in the video are naturally time-ordered, which provides a rich resource for us to mine causality knowledge from; (3) All the objects in the video can be used as context to study the contextual property of causal relations.
Unsupervised Label-aware Event Trigger and Argument Classification
Rather than relying on annotated data, our model matches the semantics of identified events with those of event type labels.
Coreference Reasoning in Machine Reading Comprehension
We propose a methodology for creating MRC datasets that better reflect the challenges of coreference reasoning and use it to create a sample evaluation set.
PABI: A Unified PAC-Bayesian Informativeness Measure for Incidental Supervision Signals
Real-world applications often require making use of {\em a range of incidental supervision signals}.
Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies
A key limitation in current datasets for multi-hop reasoning is that the required steps for answering the question are mentioned in it explicitly.
A Statistical Analysis of Summarization Evaluation Metrics using Resampling Methods
After evaluating which of the proposed methods is most appropriate for summarization through two simulation experiments, we analyze the results of applying these methods to several different automatic evaluation metrics across three sets of human annotations.
How Good (really) are Grammatical Error Correction Systems?
Standard evaluations of Grammatical Error Correction (GEC) systems make use of a fixed reference text generated relative to the original text; they show, even when using multiple references, that we have a long way to go.
Paired Examples as Indirect Supervision in Latent Decision Models
Such an objective does not require external supervision for the values of the latent output, or even the end task, yet provides an additional training signal to that provided by individual training examples themselves.
ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoning
While these tasks partially evaluate machines' ability of narrative understanding, human-like reading comprehension requires the capability to process event-based information beyond arguments and temporal reasoning.
Learning to Reason for Text Generation from Scientific Tables
In this paper, we introduce SciGen, a new challenge dataset for the task of reasoning-aware data-to-text generation consisting of tables from scientific articles and their corresponding descriptions.
Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema
To support this claim, we first show that the current evaluation method of WS is sub-optimal and propose a modification that uses twin sentences for evaluation.
Ranked #24 on
Coreference Resolution
on Winograd Schema Challenge
Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection
Despite significant progress in neural abstractive summarization, recent studies have shown that the current models are prone to generating summaries that are unfaithful to the original context.
Toward Code Generation: A Survey and Lessons from Semantic Parsing
With the growth of natural language processing techniques and demand for improved software engineering efficiency, there is an emerging interest in translating intention from human languages to programming languages.
Weighted Training for Cross-Task Learning
In this paper, we introduce Target-Aware Weighted Training (TAWT), a weighted training algorithm for cross-task learning based on minimizing a representation-based task distance between the source and target tasks.
Generalization in Instruction Following Systems
Understanding and executing natural language instructions in a grounded domain is one of the hallmarks of artificial intelligence.
Learning to Decompose and Organize Complex Tasks
Both components of our graph induction solution are evaluated in experiments, demonstrating that our models outperform a state-of-the-art text generator significantly.
Event Time Extraction and Propagation via Graph Attention Networks
Grounding events into a precise timeline is important for natural language understanding but has received limited attention in recent work.
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System
We present a new information extraction system that can automatically construct temporal event graphs from a collection of news documents from multiple sources, multiple languages (English and Spanish for our experiment), and multiple data modalities (speech, text, image and video).
MultiOpEd: A Corpus of Multi-Perspective News Editorials
We propose MultiOpEd, an open-domain news editorial corpus that supports various tasks pertaining to the argumentation structure in news editorials, focusing on automatic perspective discovery.
What is Your Article Based On? Inferring Fine-grained Provenance
We experiment with a newly created evaluation dataset, Politi-Prov, based on fact-checking articles from \url{www. politifact. com}; our experimental results show that our solution leads to a significant improvement over baselines.
Event-Centric Natural Language Processing
This tutorial targets researchers and practitioners who are interested in AI technologies that help machines understand natural language text, particularly real-world events described in the text.
Zero-shot Event Extraction via Transfer Learning: Challenges and Insights
Event extraction has long been a challenging task, addressed mostly with supervised methods that require expensive annotation and are not extensible to new event ontologies.
Learning Constraints and Descriptive Segmentation for Subevent Detection
The task of subevent detection aims to resolve this granularity issue, recognizing the membership of multi-granular events in event complexes.
Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey
Large, pre-trained transformer-based language models such as BERT have drastically changed the Natural Language Processing (NLP) field.
Incorporating Question Answering-Based Signals into Abstractive Summarization via Salient Span Selection
In this work, we propose a method for incorporating question-answering (QA) signals into a summarization model.
Design Challenges for a Multi-Perspective Search Engine
Naturally, identifying such responses within a document is a natural language understanding task.
Event Linking: Grounding Event Mentions to Wikipedia
Third, we retrain and evaluate two state-of-the-art (SOTA) entity linking models, showing the challenges of event linking, and we propose an event-specific linking system EVELINK to set a competitive result for the new task.
Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval
Retrieval-based language models (R-LM) model the probability of natural language text by combining a standard language model (LM) with examples retrieved from an external datastore at test time.
ROCK: Causal Inference Principles for Reasoning about Commonsense Causality
Commonsense causality reasoning (CCR) aims at identifying plausible causes and effects in natural language descriptions that are deemed reasonable by an average person.
Understanding Robust Generalization in Learning Regular Languages
Currently, deep neural networks struggle to generalize robustly to such shifts in the data distribution.
There is a Time and Place for Reasoning Beyond the Image
For example, in Figure 1, we can find a way to identify the news articles related to the picture through segment-wise understandings of the signs, the buildings, the crowds, and more.
Cross-modal Map Learning for Vision and Language Navigation
We consider the problem of Vision-and-Language Navigation (VLN).
Label Semantics for Few Shot Named Entity Recognition
We study the problem of few shot learning for named entity recognition.
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization
Empirical analyses show that, despite the challenging nature of generative tasks, we were able to achieve a 16. 5x model footprint compression ratio with little performance drop relative to the full-precision counterparts on multiple summarization and QA datasets.
Label Semantic Aware Pre-training for Few-shot Text Classification
Label semantic aware systems have leveraged this information for improved text classification performance during fine-tuning and prediction.
Benchmarking Answer Verification Methods for Question Answering-Based Summarization Evaluation Metrics
Question answering-based summarization evaluation metrics must automatically determine whether the QA model's prediction is correct or not, a task known as answer verification.
Re-Examining System-Level Correlations of Automatic Summarization Evaluation Metrics
How reliably an automatic summarization evaluation metric replicates human judgments of summary quality is quantified by system-level correlations.
Repro: An Open-Source Library for Improving the Reproducibility and Usability of Publicly Available Research Code
We introduce Repro, an open-source library which aims at improving the reproducibility and usability of research code.
