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Found 24 papers, 15 papers with code
Propagation and Pitfalls: Reasoning-based Assessment of Knowledge Editing through Counterfactual Tasks
Our analysis over the chain-of-thought generation of edited models further uncover key reasons behind the inadequacy of existing knowledge editing methods from a reasoning standpoint, involving aspects on fact-wise editing, fact recall ability, and coherence in generation.
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.
UNITE: A Unified Benchmark for Text-to-SQL Evaluation
A practical text-to-SQL system should generalize well on a wide variety of natural language questions, unseen database schemas, and novel SQL query structures.
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness
Neural text-to-SQL models have achieved remarkable performance in translating natural language questions into SQL queries.
DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases
Question answering over knowledge bases (KBs) aims to answer natural language questions with factual information such as entities and relations in KBs.
QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition
Recently, prompt-based learning for pre-trained language models has succeeded in few-shot Named Entity Recognition (NER) by exploiting prompts as task guidance to increase label efficiency.
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.
Virtual Augmentation Supported Contrastive Learning of Sentence Representations
We then define an instance discrimination task regarding this neighborhood and generate the virtual augmentation in an adversarial training manner.
Pairwise Supervised Contrastive Learning of Sentence Representations
Many recent successes in sentence representation learning have been achieved by simply fine-tuning on the Natural Language Inference (NLI) datasets with triplet loss or siamese loss.
Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering
However, a large amount of world's knowledge is stored in structured databases, and need to be accessed using query languages such as SQL.
Improving Factual Consistency of Abstractive Summarization via Question Answering
A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents.
Supporting Clustering with Contrastive Learning
Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space.
Ranked #1 on
Short Text Clustering
on AG News
Entity-level Factual Consistency of Abstractive Text Summarization
A key challenge for abstractive summarization is ensuring factual consistency of the generated summary with respect to the original document.
Zero-shot Generalization in Dialog State Tracking through Generative Question Answering
Dialog State Tracking (DST), an integral part of modern dialog systems, aims to track user preferences and constraints (slots) in task-oriented dialogs.
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with self-supervised learning objectives, such as Masked Language Model (MLM).
Ranked #7 on
Text-To-SQL
on spider
(Exact Match Accuracy (Dev) metric)
Enhancing Clinical BERT Embedding using a Biomedical Knowledge Base
Domain knowledge is important for building Natural Language Processing (NLP) systems for low-resource settings, such as in the clinical domain.
Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction
When multiple plausible answers are found, the system should rewrite the question for each answer to resolve the ambiguity.
Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling
Unsupervised domain adaptation addresses the problem of leveraging labeled data in a source domain to learn a well-performing model in a target domain where labels are unavailable.
Cross-Lingual Document Classification
Document Classification
+2
An Ensemble Approach for Automatic Structuring of Radiology Reports
We present an ensemble method that consolidates the predictions of three models, capturing various attributes of textual information for automatic labeling of sentences with section labels.
Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer
In this work, we define the problem of conversation structure modeling as identifying the parent utterance(s) to which each utterance in the conversation responds to.
Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text
Since the introduction of context-aware token representation techniques such as Embeddings from Language Models (ELMo) and Bidirectional Encoder Representations from Transformers (BERT), there has been numerous reports on improved performance on a variety of natural language tasks.
Clinical Concept Extraction with Contextual Word Embedding
Next, a bidirectional LSTM-CRF model is trained for clinical concept extraction using the contextual word embedding model.
Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget
Deep neural network (DNN) based approaches hold significant potential for reinforcement learning (RL) and have already shown remarkable gains over state-of-art methods in a number of applications.
Learning Policies for Markov Decision Processes from Data
We assume that the policy belongs to a class of parameterized policies which are defined using features associated with the state-action pairs.
