Search Results for author:
Found 36 papers, 20 papers with code
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.
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.
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning
In particular, tailoring GPT-2 with IPA can outperform GPT-3, while tailoring GPT- 3 with IPA brings a major performance boost over GPT-3 (and sometimes even over GPT-4).
PairReranker: Pairwise Reranking for Natural Language Generation
Pre-trained language models have been successful in natural language generation (NLG) tasks.
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.
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.
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.
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.
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.
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?
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.
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
+2
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.
Probing Commonsense Explanation in Dialogue Response Generation
Humans use commonsense reasoning (CSR) implicitly to produce natural and coherent responses in conversations.
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.
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.
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.
RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge
Question: I have five fingers but I am not alive.
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.
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.
FreeDOM: A Transferable Neural Architecture for Structured Information Extraction on Web Documents
By combining these stages, FreeDOM is able to generalize to unseen sites after training on a small number of seed sites from that vertical without requiring expensive hand-crafted features over visual renderings of the page.
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.
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.
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.
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.
LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation
Successfully training a deep neural network demands a huge corpus of labeled data.
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.
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
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.
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.
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 #22 on
Common Sense Reasoning
on CommonsenseQA
(using extra training data)
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).
Neural Adaptation Layers for Cross-domain Named Entity Recognition
Recent research efforts have shown that neural architectures can be effective in conventional information extraction tasks such as named entity recognition, yielding state-of-the-art results on standard newswire datasets.
ExtRA: Extracting Prominent Review Aspects from Customer Feedback
Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects.
Mining Cross-Cultural Differences and Similarities in Social Media
Cross-cultural differences and similarities are common in cross-lingual natural language understanding, especially for research in social media.
Automatic Extraction of Commonsense LocatedNear Knowledge
LocatedNear relation is a kind of commonsense knowledge describing two physical objects that are typically found near each other in real life.
