Search Results for author:
Found 263 papers, 76 papers with code
BabyBERTa: Learning More Grammar With Small-Scale Child-Directed Language
Transformer-based language models have taken the NLP world by storm.
``I'd rather just go to bed'': Understanding Indirect Answers
We revisit a pragmatic inference problem in dialog: Understanding indirect responses to questions.
PerKGQA: Question Answering over Personalized Knowledge Graphs
The above concerns motivate our question answer- ing setting over personalized knowledge graphs (PERKGQA) where each user has restricted access to their KG.
There’s a Time and Place for Reasoning Beyond the Image
Images are often more significant than only the pixels to human eyes, as we can infer, associate, and reason with contextual information from other sources to establish a more complete picture.
Understanding the Extent to which Content Quality 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.
ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations
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.
Capturing the Content of a Document through Complex Event Identification
We address this task as a pipeline, first predicting whether two granular events mentioned in the text belong to the same complex event, independently of their position in the text, and then using this to cluster them into complex events.
Quantifying Clinical Outcome Measures in Patients with Epilepsy Using the Electronic Health Record
A wealth of important clinical information lies untouched in the Electronic Health Record, often in the form of unstructured textual documents.
Building Low-Resource NER Models Using Non-Speaker Annotations
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
What Do Users Care About? Detecting Actionable Insights from User Feedback
Users often leave feedback on a myriad of aspects of a product which, if leveraged successfully, can help yield useful insights that can lead to further improvements down the line.
New Frontiers of Information Extraction
This tutorial targets researchers and practitioners who are interested in AI and ML technologies for structural information extraction (IE) from unstructured textual sources.
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios
We introduce RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios.
Yes, No or IDK: The Challenge of Unanswerable Yes/No Questions
For example, given the context “She married a lawyer from New-York.”, we don’t know whether the answer to the question “Did she marry in New York?” is “Yes” or “No”.
Few-Shot Novel Concept Learning for Semantic Parsing
This way the concept learning problem is naturally a program synthesis problem and our algorithm learns from a few examples to synthesize a program representing the novel concept.
Compositional Data and Task Augmentation for Instruction Following
Executing natural language instructions in a physically grounded domain requires a model that understands both spatial concepts such as “left of” and “above”, and the compositional language used to identify landmarks and articulate instructions relative to them.
On the Effects of Transformer Size on In- and Out-of-Domain Calibration
To reduce the cost of training such large models, prior work has developed smaller, more compact models which achieves a significant speedup in training time while maintaining competitive accuracy to the original model on downstream tasks.
Do We Know What We Don’t Know? Studying Unanswerable Questions beyond SQuAD 2.0
Understanding when a text snippet does not provide a sought after information is an essential part of natural language utnderstanding.
ExpertQA: Expert-Curated Questions and Attributed Answers
As language models are adapted by a more sophisticated and diverse set of users, the importance of guaranteeing that they provide factually correct information supported by verifiable sources is critical across fields of study & professions.
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning
We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data.
Building Interpretable and Reliable Open Information Retriever for New Domains Overnight
Information retrieval (IR) or knowledge retrieval, is a critical component for many down-stream tasks such as open-domain question answering (QA).
On Regularization and Inference with Label Constraints
Prior knowledge and symbolic rules in machine learning are often expressed in the form of label constraints, especially in structured prediction problems.
The Integer Linear Programming Inference Cookbook
At the end, we will see two worked examples to illustrate the use of these recipes.
Towards Open-Domain Topic Classification
We introduce an open-domain topic classification system that accepts user-defined taxonomy in real time.
Large Language Models as Sous Chefs: Revising Recipes with GPT-3
With their remarkably improved text generation and prompting capabilities, large language models can adapt existing written information into forms that are easier to use and understand.
On Learning Latent Models with Multi-Instance Weak Supervision
This condition non-trivially generalizes and relaxes the existing small ambiguity degree in the PLL literature, since we allow the transition to be deterministic.
A Static Evaluation of Code Completion by Large Language Models
Large language models trained on code have shown great potential to increase productivity of software developers.
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge
The open-ended Visual Question Answering (VQA) task requires AI models to jointly reason over visual and natural language inputs using world knowledge.
Characterizing and Measuring Linguistic Dataset Drift
In this paper, we propose three dimensions of linguistic dataset drift: vocabulary, structural, and semantic drift.
Interpretable by Design Visual Question Answering
Model interpretability has long been a hard problem for the AI community especially in the multimodal setting, where vision and language need to be aligned and reasoned at the same time.
Taxonomy Expansion for Named Entity Recognition
We experiment with 6 diverse datasets and show that PLM consistently performs better than most other approaches (0. 5 - 2. 5 F1), including in novel settings for taxonomy expansion not considered in prior work.
Open-Domain Event Graph Induction for Mitigating Framing Bias
However, none of the existing methods have explicitly addressed the issue of framing bias that is inherent in news articles.
Comparing Biases and the Impact of Multilingual Training across Multiple Languages
As a result, it is critical to examine biases within each language and attribute.
IXA/Cogcomp at SemEval-2023 Task 2: Context-enriched Multilingual Named Entity Recognition using Knowledge Bases
Named Entity Recognition (NER) is a core natural language processing task in which pre-trained language models have shown remarkable performance.
Multilingual Named Entity Recognition
named-entity-recognition
+2
Towards Corpus-Scale Discovery of Selection Biases in News Coverage: Comparing What Sources Say About Entities as a Start
To facilitate research in this domain, we propose and study a conceptual framework, where we compare how sources typically mention certain controversial entities, and use such as indicators for the sources' content selection preferences.
Conversation Style Transfer using Few-Shot Learning
Conventional text style transfer approaches focus on sentence-level style transfer without considering contextual information, and the style is described with attributes (e. g., formality).
GLUECons: A Generic Benchmark for Learning Under Constraints
Recent research has shown that integrating domain knowledge into deep learning architectures is effective -- it helps reduce the amount of required data, improves the accuracy of the models' decisions, and improves the interpretability of models.
STREET: A Multi-Task Structured Reasoning and Explanation Benchmark
We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark.
Rethinking with Retrieval: Faithful Large Language Model Inference
To address this issue, we propose a novel post-processing approach, rethinking with retrieval (RR), which retrieves relevant external knowledge based on the decomposed reasoning steps obtained from the chain-of-thought (CoT) prompting.
Ranked #2 on
Question Answering
on StrategyQA
PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition
As these propositions can carry different truth values in the context of a given premise, we argue for the need to recognize the textual entailment relation of each proposition in a sentence individually.
ReCode: Robustness Evaluation of Code Generation Models
Most existing works on robustness in text or code tasks have focused on classification, while robustness in generation tasks is an uncharted area and to date there is no comprehensive benchmark for robustness in code generation.
In and Out-of-Domain Text Adversarial Robustness via Label Smoothing
Recently it has been shown that state-of-the-art NLP models are vulnerable to adversarial attacks, where the predictions of a model can be drastically altered by slight modifications to the input (such as synonym substitutions).
CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Context
While pre-trained language models (LM) for code have achieved great success in code completion, they generate code conditioned only on the contents within the file, i. e., in-file context, but ignore the rich semantics in other files within the same project, i. e., cross-file context, a critical source of information that is especially useful in modern modular software development.
Generic Temporal Reasoning with Differential Analysis and Explanation
Temporal reasoning is the task of predicting temporal relations of event pairs.
Dialog2API: Task-Oriented Dialogue with API Description and Example Programs
The conversational model interacts with the environment by generating and executing programs triggering a set of pre-defined APIs.
Privacy Adhering Machine Un-learning in NLP
In several real world industry applications that use Machine Learning to build models on user data, such mandates require significant effort both in terms of data cleansing as well as model retraining while ensuring the models do not deteriorate in prediction quality due to removal of data.
Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale
Using a 66 billion parameter language model (OPT-66B) across a diverse set of 14 downstream tasks, we find this is indeed the case: $\sim$70% of attention heads and $\sim$20% of feed forward networks can be removed with minimal decline in task performance.
Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach
We distill several insights from our analysis on study the peer review process with the help of large LMs.
Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts
Explicit decomposition modeling, which involves breaking down complex tasks into more straightforward and often more interpretable sub-tasks, has long been a central theme in developing robust and interpretable NLU systems.
Multi-lingual Evaluation of Code Generation Models
Using these benchmarks, we are able to assess the performance of code generation models in a multi-lingual fashion, and discovered generalization ability of language models on out-of-domain languages, advantages of multi-lingual models over mono-lingual, the ability of few-shot prompting to teach the model new languages, and zero-shot translation abilities even on mono-lingual settings.
On the Limitations of Reference-Free Evaluations of Generated Text
There is significant interest in developing evaluation metrics which accurately estimate the quality of generated text without the aid of a human-written reference text, which can be time consuming and expensive to collect or entirely unavailable in online applications.
Zero-Shot On-the-Fly Event Schema Induction
The answers to these questions can be found by collecting many documents on the complex event of interest, extracting relevant information, and analyzing it.
CIKQA: Learning Commonsense Inference with a Unified Knowledge-in-the-loop QA Paradigm
We first align commonsense tasks with relevant knowledge from commonsense knowledge bases and ask humans to annotate whether the knowledge is enough or not.
Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis
Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task which involves four elements from user-generated texts: aspect term, aspect category, opinion term, and sentiment polarity.
Cross-Lingual Speaker Identification Using Distant Supervision
Speaker identification, determining which character said each utterance in literary text, benefits many downstream tasks.
Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction
In this paper, we seek to improve the faithfulness of TempRel extraction models from two perspectives.
Ranked #3 on
Temporal Relation Classification
on MATRES
Are All Steps Equally Important? Benchmarking Essentiality Detection of Events
In this paper, we seek to fill this gap by studying how well current models can understand the essentiality of different step events towards a goal event.
Unsupervised Neural Stylistic Text Generation using Transfer learning and Adapters
Research has shown that personality is a key driver to improve engagement and user experience in conversational systems.
Human-guided Collaborative Problem Solving: A Natural Language based Framework
We consider the problem of human-machine collaborative problem solving as a planning task coupled with natural language communication.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner
Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises.
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.
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.
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.
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.
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 Semantics for Few Shot Named Entity Recognition
We study the problem of few shot learning for named entity recognition.
Cross-modal Map Learning for Vision and Language Navigation
We consider the problem of Vision-and-Language Navigation (VLN).
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.
Understanding Robust Generalization in Learning Regular Languages
Currently, deep neural networks struggle to generalize robustly to such shifts in the data distribution.
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.
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.
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.
Design Challenges for a Multi-Perspective Search Engine
Naturally, identifying such responses within a document is a natural language understanding task.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
Generalization in Instruction Following Systems
Understanding and executing natural language instructions in a grounded domain is one of the hallmarks of artificial intelligence.
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.
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.
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.
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.
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.
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.
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.
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.
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}.
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.
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.
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.
QANom: Question-Answer driven SRL for Nominalizations
We propose a new semantic scheme for capturing predicate-argument relations for nominalizations, termed QANom.
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.
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.
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.
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.
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.
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.
"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.
Joint Constrained Learning for Event-Event Relation Extraction
Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other.
Do Language Embeddings Capture Scales?
Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense, and factual knowledge.
"I'd rather just go to bed": Understanding Indirect Answers
We revisit a pragmatic inference problem in dialog: understanding indirect responses to questions.
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.
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.
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)
Task-Oriented Dialogue as Dataflow Synthesis
We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph.
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.
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.
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.
``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.
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
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.
Foreseeing the Benefits of Incidental Supervision
Real-world applications often require improved models by leveraging a range of cheap incidental supervision signals.
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.
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.
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.
Temporal Common Sense Acquisition with Minimal Supervision
Temporal common sense (e. g., duration and frequency of events) is crucial for understanding natural language.
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.
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.
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.
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.
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 #9 on
Named Entity Recognition (NER)
on WNUT 2017
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.
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.
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.
KnowSemLM: A Knowledge Infused Semantic Language Model
Story understanding requires developing expectations of what events come next in text.
``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.
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.
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.