Search Results for author:
Found 186 papers, 114 papers with code
Hierarchical Graph Representation Learning with Differentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.
Ranked #1 on
Graph Classification
on REDDIT-MULTI-12K
Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs
The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp.
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.
Automated Phrase Mining from Massive Text Corpora
As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus.
Empower Sequence Labeling with Task-Aware Neural Language Model
In this study, we develop a novel neural framework to extract abundant knowledge hidden in raw texts to empower the sequence labeling task.
Ranked #13 on
Part-Of-Speech Tagging
on Penn Treebank
LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion
We present LLM-Blender, an ensembling framework designed to attain consistently superior performance by leveraging the diverse strengths of multiple open-source large language models (LLMs).
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences.
Learning Named Entity Tagger using Domain-Specific Dictionary
Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features.
Indirect Supervision for Relation Extraction using Question-Answer Pairs
However, due to the incompleteness of knowledge bases and the context-agnostic labeling, the training data collected via distant supervision (DS) can be very noisy.
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
We propose a novel domain-independent framework, called CoType, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations.
Ranked #11 on
Relation Extraction
on NYT11-HRL
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life.
Ranked #29 on
Common Sense Reasoning
on CommonsenseQA
(using extra training data)
Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering
Existing work on augmenting question answering (QA) models with external knowledge (e. g., knowledge graphs) either struggle to model multi-hop relations efficiently, or lack transparency into the model's prediction rationale.
CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP
Humans can learn a new language task efficiently with only few examples, by leveraging their knowledge obtained when learning prior tasks.
Self-Discover: Large Language Models Self-Compose Reasoning Structures
We introduce SELF-DISCOVER, a general framework for LLMs to self-discover the task-intrinsic reasoning structures to tackle complex reasoning problems that are challenging for typical prompting methods.
Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models
Human and metrics evaluation on both LSTM models and BERT Transformer models on multiple datasets show that our algorithms outperform prior hierarchical explanation algorithms.
Discretized Integrated Gradients for Explaining Language Models
As a prominent attribution-based explanation algorithm, Integrated Gradients (IG) is widely adopted due to its desirable explanation axioms and the ease of gradient computation.
TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
In this paper, we introduce "entity triggers," an effective proxy of human explanations for facilitating label-efficient learning of NER models.
Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling
Many efforts have been made to facilitate natural language processing tasks with pre-trained language models (LMs), and brought significant improvements to various applications.
Ranked #47 on
Named Entity Recognition (NER)
on CoNLL 2003 (English)
Hierarchical Text Classification with Reinforced Label Assignment
While existing hierarchical text classification (HTC) methods attempt to capture label hierarchies for model training, they either make local decisions regarding each label or completely ignore the hierarchy information during inference.
Ranked #1 on
Text Classification
on RCV1
(Macro F1 metric)
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
In this paper, we present a constrained text generation task, CommonGen associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning.
Ranked #1 on
Text Generation
on CommonGen
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases.
Time-Series Event Prediction with Evolutionary State Graph
In this paper, we present evolutionary state graph, a dynamic graph structure designed to systematically represent the evolving relations (edges) among states (nodes) along time.
Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach
These annotations, referred as heterogeneous supervision, often conflict with each other, which brings a new challenge to the original relation extraction task: how to infer the true label from noisy labels for a given instance.
HMEAE: Hierarchical Modular Event Argument Extraction
Existing event extraction methods classify each argument role independently, ignoring the conceptual correlations between different argument roles.
Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of Snapchat
Here we propose to formalize individual user's in-app action transition patterns as a temporally evolving action graph, and analyze its characteristics in terms of informing future user engagement.
Commonsense-Focused Dialogues for Response Generation: An Empirical Study
Moreover, existing dialogue datasets do not explicitly focus on exhibiting commonsense as a facet.
Dataless Knowledge Fusion by Merging Weights of Language Models
In this paper, we study the problem of merging individual models built on different training data sets to obtain a single model that performs well both across all data set domains and can generalize on out-of-domain data.
End-to-End Reinforcement Learning for Automatic Taxonomy Induction
We present a novel end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms.
Mining Entity Synonyms with Efficient Neural Set Generation
Mining entity synonym sets (i. e., sets of terms referring to the same entity) is an important task for many entity-leveraging applications.
Jointly Learning Explainable Rules for Recommendation with Knowledge Graph
The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue.
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
Current systems of fine-grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions.
Collaborative Policy Learning for Open Knowledge Graph Reasoning
We propose a novel reinforcement learning framework to train two collaborative agents jointly, i. e., a multi-hop graph reasoner and a fact extractor.
NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction
The soft matching module learns to match rules with semantically similar sentences such that raw corpora can be automatically labeled and leveraged by the RE module (in a much better coverage) as augmented supervision, in addition to the exactly matched sentences.
Learning Dynamic Context Augmentation for Global Entity Linking
Despite of the recent success of collective entity linking (EL) methods, these "global" inference methods may yield sub-optimal results when the "all-mention coherence" assumption breaks, and often suffer from high computational cost at the inference stage, due to the complex search space.
Ranked #5 on
Entity Disambiguation
on AIDA-CoNLL
ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning
While pre-trained language models (PTLMs) have achieved noticeable success on many NLP tasks, they still struggle for tasks that require event temporal reasoning, which is essential for event-centric applications.
Ranked #1 on
Question Answering
on Torque
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER
We also find that good demonstration can save many labeled examples and consistency in demonstration contributes to better performance.
Automatic Synonym Discovery with Knowledge Bases
In this paper, we study the problem of automatic synonym discovery with knowledge bases, that is, identifying synonyms for knowledge base entities in a given domain-specific corpus.
Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction
In this paper, we study the problem what limits the performance of DS-trained neural models, conduct thorough analyses, and identify a factor that can influence the performance greatly, shifted label distribution.
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models
Large pre-trained vision-language (VL) models can learn a new task with a handful of examples and generalize to a new task without fine-tuning.
Ranked #4 on
Image Captioning
on Flickr30k Captions test
(SPICE metric)
Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning
Walk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions.
Integrating Local Context and Global Cohesiveness for Open Information Extraction
However, current Open IE systems focus on modeling local context information in a sentence to extract relation tuples, while ignoring the fact that global statistics in a large corpus can be collectively leveraged to identify high-quality sentence-level extractions.
Learning Dual Retrieval Module for Semi-supervised Relation Extraction
In this paper, we leverage a key insight that retrieving sentences expressing a relation is a dual task of predicting relation label for a given sentence---two tasks are complementary to each other and can be optimized jointly for mutual enhancement.
Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering
In this paper, we augment a general commonsense QA framework with a knowledgeable path generator.
Contextualizing Hate Speech Classifiers with Post-hoc Explanation
Hate speech classifiers trained on imbalanced datasets struggle to determine if group identifiers like "gay" or "black" are used in offensive or prejudiced ways.
SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble
In this study, we propose a novel framework, SetExpan, which tackles this problem, with two techniques: (1) a context feature selection method that selects clean context features for calculating entity-entity distributional similarity, and (2) a ranking-based unsupervised ensemble method for expanding entity set based on denoised context features.
SCOTT: Self-Consistent Chain-of-Thought Distillation
While CoT can yield dramatically improved performance, such gains are only observed for sufficiently large LMs.
Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs
Existing techniques either cannot be scaled to large-scale bipartite graphs that have limited labels or cannot exploit the unique structure of bipartite graphs, which have distinct node features in two domains.
Pre-training Text-to-Text Transformers for Concept-centric Common Sense
Pre-trained language models (PTLM) have achieved impressive results in a range of natural language understanding (NLU) and generation (NLG) tasks.
Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation
We study the power of cross-attention in the Transformer architecture within the context of transfer learning for machine translation, and extend the findings of studies into cross-attention when training from scratch.
Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning
In addition, we also create two new datasets, X-CSQA and X-CODAH, by translating their English versions to 15 other languages, so that we can evaluate popular ML-LMs for cross-lingual commonsense reasoning.
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction
An extractive rationale explains a language model's (LM's) prediction on a given task instance by highlighting the text inputs that most influenced the prediction.
Unsupervised Cross-Task Generalization via Retrieval Augmentation
Humans can perform unseen tasks by recalling relevant skills acquired previously and then generalizing them to the target tasks, even if there is no supervision at all.
Faith and Fate: Limits of Transformers on Compositionality
We formulate compositional tasks as computation graphs to systematically quantify the level of complexity, and break down reasoning steps into intermediate sub-procedures.
An Attention-based Collaboration Framework for Multi-View Network Representation Learning
Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network.
Visually Grounded Continual Learning of Compositional Phrases
To study this human-like language acquisition ability, we present VisCOLL, a visually grounded language learning task, which simulates the continual acquisition of compositional phrases from streaming visual scenes.
Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution
We show that if the information contained in the graph and the time series data are closely related, then this inter-dependence can be used to predict the time series with improved accuracy.
Learning from Explanations with Neural Execution Tree
While deep neural networks have achieved impressive performance on a range of NLP tasks, these data-hungry models heavily rely on labeled data, which restricts their applications in scenarios where data annotation is expensive.
Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation
Despite significant progress, state-of-the-art abstractive summarization methods are still prone to hallucinate content inconsistent with the source document.
Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation
In this paper, we present a systematic analysis that studies whether current seq2seq models, especially pre-trained language models, are good enough for preserving important input concepts and to what extent explicitly guiding generation with the concepts as lexical constraints is beneficial.
NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challenge
News article revision histories provide clues to narrative and factual evolution in news articles.
NewsEdits: A News Article Revision Dataset and a Novel Document-Level Reasoning Challenge
News article revision histories provide clues to narrative and factual evolution in news articles.
Learning Contextualized Knowledge Structures for Commonsense Reasoning
Recently, knowledge graph (KG) augmented models have achieved noteworthy success on various commonsense reasoning tasks.
Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement
The ability to derive underlying principles from a handful of observations and then generalize to novel situations -- known as inductive reasoning -- is central to human intelligence.
Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction
Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations.
Gradient-based Editing of Memory Examples for Online Task-free Continual Learning
We explore task-free continual learning (CL), in which a model is trained to avoid catastrophic forgetting in the absence of explicit task boundaries or identities.
Raw-to-End Name Entity Recognition in Social Media
Our model neither requires the conversion from character sequences to word sequences, nor assumes tokenizer can correctly detect all word boundaries.
SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning
and fine (Which nodes/paths in the KG are useful?)
FaiRR: Faithful and Robust Deductive Reasoning over Natural Language
Recent works show that such models can also produce the reasoning steps (i. e., the proof graph) that emulate the model's logical reasoning process.
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning
While extreme-scale language models have demonstrated exceptional performance on a variety of language tasks, the degree of control over these language models through pure prompting can often be limited.
Learning to Generate Task-Specific Adapters from Task Description
Recent study further shows that they can learn to generalize to novel tasks, by including task descriptions as part of the source sequence and training the model with (source, target) examples.
Refining Language Models with Compositional Explanations
However, such a regularization technique lacks flexibility and coverage, since only importance scores towards a pre-defined list of features are adjusted, while more complex human knowledge such as feature interaction and pattern generalization can hardly be incorporated.
X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering
We extensively evaluate our framework on two challenging cross-lingual NLU tasks: multilingual task-oriented dialog and typologically diverse question answering.
Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling
Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario.
Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling
Its performance is largely influenced by the annotation quality and quantity in supervised learning scenarios, and obtaining ground truth labels is often costly.
IsoBN: Fine-Tuning BERT with Isotropic Batch Normalization
Fine-tuning pre-trained language models (PTLMs), such as BERT and its better variant RoBERTa, has been a common practice for advancing performance in natural language understanding (NLU) tasks.
Sparse Distillation: Speeding Up Text Classification by Using Bigger Student Models
Distilling state-of-the-art transformer models into lightweight student models is an effective way to reduce computation cost at inference time.
DOMINO: A Dual-System for Multi-step Visual Language Reasoning
By fine-tuning the System-2 module (LLaMA-2 70B) on only a small amount of data on multi-step reasoning, the accuracy of our method is further improved and surpasses the best fully-supervised end-to-end approach by 5. 7% and a pipeline approach with FlanPaLM (540B) by 7. 5% on a challenging dataset with human-authored questions.
Teaching Machine Comprehension with Compositional Explanations
Advances in machine reading comprehension (MRC) rely heavily on the collection of large scale human-annotated examples in the form of (question, paragraph, answer) triples.
On the Robustness of Reading Comprehension Models to Entity Renaming
We study the robustness of machine reading comprehension (MRC) models to entity renaming -- do models make more wrong predictions when the same questions are asked about an entity whose name has been changed?
Contextualized Scene Imagination for Generative Commonsense Reasoning
In this paper, we propose an Imagine-and-Verbalize (I&V) method, which learns to imagine a relational scene knowledge graph (SKG) with relations between the input concepts, and leverage the SKG as a constraint when generating a plausible scene description.
REV: Information-Theoretic Evaluation of Free-Text Rationales
More concretely, we propose a metric called REV (Rationale Evaluation with conditional V-information), to quantify the amount of new, label-relevant information in a rationale beyond the information already available in the input or the label.
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning
Additionally, the explicit redundancy measure in MMR helps the neural representation of the summary to better capture redundancy.
RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models
To audit the robustness of named entity recognition (NER) models, we propose RockNER, a simple yet effective method to create natural adversarial examples.
Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection
To demonstrate the threat, we propose a simple method to perform VPI by poisoning the model's instruction tuning data, which proves highly effective in steering the LLM.
Facet-Aware Evaluation for Extractive Summarization
In this paper, we present a facet-aware evaluation setup for better assessment of the information coverage in extracted summaries.
BITE: Textual Backdoor Attacks with Iterative Trigger Injection
We propose BITE, a backdoor attack that poisons the training data to establish strong correlations between the target label and a set of "trigger words".
Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks
We introduce Retweet-BERT, a simple and scalable model to estimate the political leanings of Twitter users.
PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning
In addition, we introduce a novel task, Counterfactual Planning, that requires a revision of a plan to cope with a counterfactual situation.
Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs
Our analysis of GPT-series models over a rule subset reveals significant gaps in LLMs' logic understanding compared to human performance, especially in compositional and structural complex rules with certain bias patterns.
Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning
The ability to continuously expand knowledge over time and utilize it to rapidly generalize to new tasks is a key feature of human linguistic intelligence.
PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales
Neural language models (LMs) have achieved impressive results on various language-based reasoning tasks by utilizing latent knowledge encoded in their own pretrained parameters.
NS3: Neuro-Symbolic Semantic Code Search
We compare our model - NS3 (Neuro-Symbolic Semantic Search) - to a number of baselines, including state-of-the-art semantic code retrieval methods, and evaluate on two datasets - CodeSearchNet and Code Search and Question Answering.
Eliciting Knowledge from Experts:Automatic Transcript Parsing for Cognitive Task Analysis
To reduce human efforts and scale the process, automated CTA transcript parsing is desirable.
Reporting the Unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes
Official reports of hate crimes in the US are under-reported relative to the actual number of such incidents.
Cross-lingual Lifelong Learning
The longstanding goal of multi-lingual learning has been to develop a universal cross-lingual model that can withstand the changes in multi-lingual data distributions.
RobustLR: Evaluating Robustness to Logical Perturbation in Deductive Reasoning
Transformers have been shown to be able to perform deductive reasoning on a logical rulebase containing rules and statements written in English natural language.
Are Machine Rationales (Not) Useful to Humans? Measuring and Improving Human Utility of Free-Text Rationales
Existing metrics like task performance of the LM generating the rationales, or similarity between generated and gold rationales are not good indicators of their human utility.
In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search
We further use the data generated by LINK to construct a dataset Logic-Induced-Long-Tail (LINT) that can be used to evaluate downstream models on the long-tail distribution; LINT contains 108K knowledge statements spanning four domains.
Eliciting Knowledge from Experts: Automatic Transcript Parsing for Cognitive Task Analysis
To reduce human efforts and scale the process, automated CTA transcript parsing is desirable.
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
Knowledge graphs (KGs) have helped neural models improve performance on various knowledge-intensive tasks, like question answering and item recommendation.
Tailoring Self-Rationalizers with Multi-Reward Distillation
Results on five difficult question-answering datasets StrategyQA, QuaRel, OpenBookQA, NumerSense and QASC show that not only does MaRio improve task accuracy, but it also improves the self-rationalization quality of small LMs across the aforementioned axes better than a supervised fine-tuning (SFT) baseline.
Do Language Models Perform Generalizable Commonsense Inference?
Inspired by evidence that pretrained language models (LMs) encode commonsense knowledge, recent work has applied LMs to automatically populate commonsense knowledge graphs (CKGs).
ER-Test: Evaluating Explanation Regularization Methods for Language Models
to align with human rationales (Which input tokens would humans focus on?).
Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large Language Models
In many task settings, text classification models are likely to encounter examples from novel classes on which they cannot predict correctly.
Eliciting and Understanding Cross-Task Skills with Task-Level Mixture-of-Experts
Recent works suggest that transformer models are capable of multi-tasking on diverse NLP tasks and adapting to new tasks efficiently.
How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bench
We investigate the predictability of large language model (LLM) capabilities: given records of past experiments using different model families, numbers of parameters, tasks, and numbers of in-context examples, can we accurately predict LLM performance on new experiment configurations?
KCAT: A Knowledge-Constraint Typing Annotation Tool
Fine-grained Entity Typing is a tough task which suffers from noise samples extracted from distant supervision.
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks
Increasing concerns and regulations about data privacy and sparsity necessitate the study of privacy-preserving, decentralized learning methods for natural language processing (NLP) tasks.
Machine Translation Robustness to Natural Asemantic Variation
We find that existing MT models fail when presented with NAV data, but we demonstrate strategies to improve performance on NAV by fine-tuning them with human-generated variations.
Estimating Large Language Model Capabilities without Labeled Test Data
In this paper, we propose the task of ICL accuracy estimation, in which we predict the accuracy of an LLM when doing in-context learning on a new task given only unlabeled test data for that task.
Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step
We release our corpus of chain-of-thought samples and code.
CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting
As the utilization of large language models (LLMs) has proliferated worldwide, it is crucial for them to have adequate knowledge and fair representation for diverse global cultures.
Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning
We propose a novel co-training framework with a distributional module and a pattern module.
MetaPAD: Meta Pattern Discovery from Massive Text Corpora
We propose an efficient framework, called MetaPAD, which discovers meta patterns from massive corpora with three techniques: (1) it develops a context-aware segmentation method to carefully determine the boundaries of patterns with a learnt pattern quality assessment function, which avoids costly dependency parsing and generates high-quality patterns; (2) it identifies and groups synonymous meta patterns from multiple facets---their types, contexts, and extractions; and (3) it examines type distributions of entities in the instances extracted by each group of patterns, and looks for appropriate type levels to make discovered patterns precise.
Scalable Construction and Reasoning of Massive Knowledge Bases
In today{'}s information-based society, there is abundant knowledge out there carried in the form of natural language texts (e. g., news articles, social media posts, scientific publications), which spans across various domains (e. g., corporate documents, advertisements, legal acts, medical reports), which grows at an astonishing rate.
Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering
Recently, distant supervision has gained great success on Fine-grained Entity Typing (FET).
Posterior-regularized REINFORCE for Instance Selection in Distant Supervision
This paper provides a new way to improve the efficiency of the REINFORCE training process.
AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging
We introduce an open-source web-based data annotation framework (AlpacaTag) for sequence tagging tasks such as named-entity recognition (NER).
HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion
Taxonomies are of great value to many knowledge-rich applications.
Improving BERT Fine-tuning with Embedding Normalization
Large pre-trained sentence encoders like BERT start a new chapter in natural language processing.
Mining News Events from Comparable News Corpora: A Multi-Attribute Proximity Network Modeling Approach
This proximity network captures the corpus-level co-occurence statistics for candidate event descriptors, event attributes, as well as their connections.
Generating Natural Language Adversarial Examples on a Large Scale with Generative Models
It can attack text classification models with a higher success rate than existing methods, and provide acceptable quality for humans in the meantime.
LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation
Successfully training a deep neural network demands a huge corpus of labeled data.
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data
In this work, we aim to formulate a task, construct a dataset, and provide benchmarks for developing methods for event forecasting with large volumes of unstructured text data.
RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms
Pre-trained language models (PTLMs) have achieved impressive performance on commonsense inference benchmarks, but their ability to employ commonsense to make robust inferences, which is crucial for effective communications with humans, is debated.
Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models
Recent works show that pre-trained language models (PTLMs), such as BERT, possess certain commonsense and factual knowledge.
Screenplay Quality Assessment: Can We Predict Who Gets Nominated?
Toward that goal, in this work, we present a method to evaluate the quality of a screenplay based on linguistic cues.
Two Step Joint Model for Drug Drug Interaction Extraction
When patients need to take medicine, particularly taking more than one kind of drug simultaneously, they should be alarmed that there possibly exists drug-drug interaction.
Drug–drug Interaction Extraction
named-entity-recognition
+4
SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery
To facilitate the research on studying the interplays of these two tasks, we create the first large-scale Synonym-Enhanced Set Expansion (SE2) dataset via crowdsourcing.
Learning Contextualized Knowledge Graph Structures for Commonsense Reasoning
Recently, neural-symbolic architectures have achieved success on commonsense reasoning through effectively encoding relational structures retrieved from external knowledge graphs (KGs) and obtained state-of-the-art results in tasks such as (commonsense) question answering and natural language inference.
Pre-training Text-to-Text Transformers to Write and Reason with Concepts
To augment PTLMs with common sense, we propose generative and contrastive objectives as intermediate self-supervised pre-training tasks between general pre-training and downstream task-specific fine-tuning.
Efficient Learning of Less Biased Models with Transfer Learning
Prediction bias in machine learning models, referring to undesirable model behaviors that discriminates inputs mentioning or produced by certain group, has drawn increasing attention from the research community given its societal impact.
Will This Idea Spread Beyond Academia? Understanding Knowledge Transfer of Scientific Concepts across Text Corpora
We extract scientific concepts (i. e., phrases) from corpora as instantiations of "research ideas", create concept-level features as motivated by literature, and then follow the trajectories of over 450, 000 new concepts (emerged from 1995-2014) to identify factors that lead only a small proportion of these ideas to be used in inventions and drug trials.
One-shot Learning for Temporal Knowledge Graphs
Most real-world knowledge graphs are characterized by a long-tail relation frequency distribution where a significant fraction of relations occurs only a handful of times.
On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning
Fine-tuned language models have been shown to exhibit biases against protected groups in a host of modeling tasks such as text classification and coreference resolution.
Differentiable Open-Ended Commonsense Reasoning
As a step towards making commonsense reasoning research more realistic, we propose to study open-ended commonsense reasoning (OpenCSR) -- the task of answering a commonsense question without any pre-defined choices -- using as a resource only a corpus of commonsense facts written in natural language.
Fair Hate Speech Detection through Evaluation of Social Group Counterfactuals
Counterfactual token fairness for a mentioned social group evaluates the model's predictions as to whether they are the same for (a) the actual sentence and (b) a counterfactual instance, which is generated by changing the mentioned social group in the sentence.
Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs
The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp.
Studying Strategically: Learning to Mask for Closed-book QA
Thus, our policy packs task-relevant knowledge into the parameters of a language model.
RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge
Question: I have five fingers but I am not alive.
MSD: Saliency-aware Knowledge Distillation for Multimodal Understanding
The idea aims at mimicking a teacher's modality-specific predictions by introducing auxiliary loss terms for each modality.
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources
In addition, we analyze two downstream models that use ConceptNet as a source for commonsense knowledge and find the existence of biases in those models as well.
On the Influence of Masking Policies in Intermediate Pre-training
Current NLP models are predominantly trained through a two-stage "pre-train then fine-tune" pipeline.
Probing Commonsense Explanation in Dialogue Response Generation
Humans use commonsense reasoning (CSR) implicitly to produce natural and coherent responses in conversations.
AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding
However, this approach constrains knowledge sharing across different attributes.
TaxoClass: Hierarchical Multi-Label Text Classification Using Only Class Names
Hierarchical multi-label text classification (HMTC) aims to tag each document with a set of classes from a taxonomic class hierarchy.
Multi Label Text Classification
Multi-Label Text Classification
+3
Improving Counterfactual Generation for Fair Hate Speech Detection
By applying logit pairing to equalize outcomes on the restricted set of counterfactuals for each instance, we improve fairness metrics while preserving model performance on hate speech detection.
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction
Deep neural models for named entity recognition (NER) have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations.
Low Resource Named Entity Recognition
named-entity-recognition
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KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering
The recent proposed Fusion-in-Decoder (FiD), which is built on top of the pretrained generative model T5, achieves the state-of-the-art performance in the reading module.
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora
We evaluate PTLM's ability to adapt to new corpora while retaining learned knowledge in earlier corpora.
Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation
Implicit knowledge, such as common sense, is key to fluid human conversations.
Modality-specific Distillation
In this paper, we propose modality-specific distillation (MSD) to effectively transfer knowledge from a teacher on multimodal datasets.
Using Word Embedding to Reveal Monetary Policy Explanation Changes
Documents have been an essential tool of communication for governments to announce their policy operations.
Think Before You Speak: Learning to Generate Implicit Knowledge for Response Generation by Self-Talk
We further investigate can such models identify when to generate implicit background knowledge and when it is not necessary.
End-to-End Hierarchical Text Classification with Label Assignment Policy
We present an end-to-end reinforcement learning approach to hierarchical text classification where documents are labeled by placing them at the right positions in a given hierarchy.
Recurrent Event Network : Global Structure Inference Over Temporal Knowledge Graph
We present Recurrent Event Network (RE-Net), a novel autoregressive architecture for modeling temporal sequences of multi-relational graphs (e. g., temporal knowledge graph), which can perform sequential, global structure inference over future time stamps to predict new events.
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer
Pre-trained language models are still far from human performance in tasks that need understanding of properties (e. g. appearance, measurable quantity) and affordances of everyday objects in the real world since the text lacks such information due to reporting bias.
On Continual Model Refinement in Out-of-Distribution Data Streams
Real-world natural language processing (NLP) models need to be continually updated to fix the prediction errors in out-of-distribution (OOD) data streams while overcoming catastrophic forgetting.
Knowledge-Augmented Methods for Natural Language Processing
Knowledge in natural language processing (NLP) has been a rising trend especially after the advent of large scale pre-trained models.
FRAME: Evaluating Rationale-Label Consistency Metrics for Free-Text Rationales
Following how humans communicate, free-text rationales aim to use natural language to explain neural language model (LM) behavior.
ER-TEST Evaluating Explanation Regularization Methods for NLP Models
For the latter, explanation regularization (ER) aims to improve NLM generalization by pushing the machine rationales to align with human rationales.
Curriculum Learning for Data-Efficient Vision-Language Alignment
Aligning image and text encoders from scratch using contrastive learning requires large amounts of paired image-text data.
On Grounded Planning for Embodied Tasks with Language Models
Language models (LMs) have demonstrated their capability in possessing commonsense knowledge of the physical world, a crucial aspect of performing tasks in everyday life.
MMGA: Multimodal Learning with Graph Alignment
In this paper, we propose MMGA (Multimodal learning with Graph Alignment), a novel multimodal pre-training framework to incorporate information from graph (social network), image and text modalities on social media to enhance user representation learning.
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models
Explanation-based model debugging aims to resolve spurious biases by showing human users explanations of model behavior, asking users to give feedback on the behavior, then using the feedback to update the model.
Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality
Human communication relies on common ground (CG), the mutual knowledge and beliefs shared by participants, to produce coherent and interesting conversations.
KNIFE: Distilling Reasoning Knowledge From Free-Text Rationales
First, KNIFE finetunes a teacher LM (given task input and FTR) to predict the task output, transferring reasoning knowledge from the FTRs to the teacher's hidden states.
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning
Logical reasoning of text is an important ability that requires understanding the information present in the text, their interconnections, and then reasoning through them to infer new conclusions.
PairReranker: Pairwise Reranking for Natural Language Generation
Pre-trained language models have been successful in natural language generation (NLG) tasks.
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons
We propose a novel task, G4C, to study teacher-student natural language interactions in a goal-driven and grounded environment.
Exploring Distributional Shifts in Large Language Models for Code Analysis
We systematically study how three large language models with code capabilities - CodeT5, Codex, and ChatGPT - generalize to out-of-domain data.
Design of Reconfigurable Intelligent Surfaces for Wireless Communication: A Review
The prototype undergoes rigorous empirical evaluation, encompassing multi-hop RIS signal amplification, image reconstruction, and real-world indoor signal coverage experiments.
GRILL: Grounded Vision-language Pre-training via Aligning Text and Image Regions
Generalization to unseen tasks is an important ability for few-shot learners to achieve better zero-/few-shot performance on diverse tasks.
Instruction-following Evaluation through Verbalizer Manipulation
We conduct a comprehensive evaluation of four major model families across nine datasets, employing twelve sets of verbalizers for each of them.
How FaR Are Large Language Models From Agents with Theory-of-Mind?
We propose a new evaluation paradigm for large language models (LLMs): Thinking for Doing (T4D), which requires models to connect inferences about others' mental states to actions in social scenarios.
Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance
Instead, our approach BOSS (BOotStrapping your own Skills) learns to accomplish new tasks by performing "skill bootstrapping," where an agent with a set of primitive skills interacts with the environment to practice new skills without receiving reward feedback for tasks outside of the initial skill set.
Wireless Communications in Cavity: A Reconfigurable Boundary Modulation based Approach
This paper explores the potential wireless communication applications of Reconfigurable Intelligent Surfaces (RIS) in reverberant wave propagation environments.
SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks
The Swift module is a small encoder-decoder LM fine-tuned on the oracle agent's action trajectories, while the Sage module employs LLMs such as GPT-4 for subgoal planning and grounding.
Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertainty
As natural language becomes the default interface for human-AI interaction, there is a critical need for LMs to appropriately communicate uncertainties in downstream applications.
What Will My Model Forget? Forecasting Forgotten Examples in Language Model Refinement
We propose a partially interpretable forecasting model based on the observation that changes in pre-softmax logit scores of pretraining examples resemble that of online learned examples, which performs decently on BART but fails on T5 models.
Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification
Finally, we use this model to filter out inconsistent model-generated rationales in self-consistency decoding, resulting in a 6% accuracy improvement on average across three MCQ datasets.
WinoViz: Probing Visual Properties of Objects Under Different States
We present WinoViz, a text-only evaluation dataset, consisting of 1, 380 examples that probe the reasoning abilities of language models regarding variant visual properties of objects under different contexts or states.
Logits of API-Protected LLMs Leak Proprietary Information
The commercialization of large language models (LLMs) has led to the common practice of high-level API-only access to proprietary models.
