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Found 22 papers, 14 papers with code
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.
TableFormer: Robust Transformer Modeling for Table-Text Encoding
Existing models for table understanding require linearization of the table structure, where row or column order is encoded as an unwanted bias.
Graph-Based Decoding for Task Oriented Semantic Parsing
The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders.
QA-Driven Zero-shot Slot Filling with Weak Supervision Pretraining
To address this problem, we introduce QA-driven slot filling (QASF), which extracts slot-filler spans from utterances with a span-based QA model.
TIMEDIAL: Temporal Commonsense Reasoning in Dialog
In this paper, we present the first study to investigate pre-trained LMs for their temporal reasoning capabilities in dialogs by introducing a new task and a crowd-sourced English challenge set, TIMEDIAL.
Few-shot Intent Classification and Slot Filling with Retrieved Examples
Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce domain.
Neural Data Augmentation via Example Extrapolation
In many applications of machine learning, certain categories of examples may be underrepresented in the training data, causing systems to underperform on such "few-shot" cases at test time.
Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements
We propose widget captioning, a novel task for automatically generating language descriptions for UI elements from multimodal input including both the image and the structural representations of user interfaces.
Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension
Reading comprehension models have been successfully applied to extractive text answers, but it is unclear how best to generalize these models to abstractive numerical answers.
Ranked #4 on
Question Answering
on DROP Test
A General Framework for Information Extraction using Dynamic Span Graphs
We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs.
Ranked #1 on
Relation Extraction
on ACE 2004
(Cross Sentence metric)
Joint Entity and Relation Extraction
Named Entity Recognition (NER)
PAWS: Paraphrase Adversaries from Word Scrambling
Existing paraphrase identification datasets lack sentence pairs that have high lexical overlap without being paraphrases.
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles.
Ranked #7 on
Joint Entity and Relation Extraction
on SciERC
Coreference Resolution
Joint Entity and Relation Extraction
+1
Large-Scale QA-SRL Parsing
We present a new large-scale corpus of Question-Answer driven Semantic Role Labeling (QA-SRL) annotations, and the first high-quality QA-SRL parser.
Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling
Recent BIO-tagging-based neural semantic role labeling models are very high performing, but assume gold predicates as part of the input and cannot incorporate span-level features.
Higher-order Coreference Resolution with Coarse-to-fine Inference
We introduce a fully differentiable approximation to higher-order inference for coreference resolution.
Ranked #14 on
Coreference Resolution
on OntoNotes
Crowdsourcing Question-Answer Meaning Representations
We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs.
End-to-end Neural Coreference Resolution
We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector.
Ranked #15 on
Coreference Resolution
on CoNLL 2012
Deep Semantic Role Labeling: What Works and What's Next
We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations.
Ranked #2 on
Predicate Detection
on CoNLL 2005
