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Found 20 papers, 14 papers with code
TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering
We introduce TIFA (Text-to-Image Faithfulness evaluation with question Answering), an automatic evaluation metric that measures the faithfulness of a generated image to its text input via visual question answering (VQA).
HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot Generalisation
By converting instructions into modules, HINT models can effectively disregard the length of instructions and few-shot example inputs in terms of compute usage.
Self-Instruct: Aligning Language Model with Self Generated Instructions
Applying our method to vanilla GPT3, we demonstrate a 33% absolute improvement over the original model on Super-NaturalInstructions, on par with the performance of InstructGPT_001, which is trained with private user data and human annotations.
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets.
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions -- training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.
Probing Across Time: What Does RoBERTa Know and When?
Models of language trained on very large corpora have been demonstrated useful for NLP.
MultiModalQA: Complex Question Answering over Text, Tables and Images
When answering complex questions, people can seamlessly combine information from visual, textual and tabular sources.
Automated Lay Language Summarization of Biomedical Scientific Reviews
Health literacy has emerged as a crucial factor in making appropriate health decisions and ensuring treatment outcomes.
LiveQA: A Question Answering Dataset over Sports Live
In this paper, we introduce LiveQA, a new question answering dataset constructed from play-by-play live broadcast.
Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics
Experiments across four datasets show that these model-dependent measures reveal three distinct regions in the data map, each with pronounced characteristics.
Do NLP Models Know Numbers? Probing Numeracy in Embeddings
The ability to understand and work with numbers (numeracy) is critical for many complex reasoning tasks.
Machine Reading Comprehension: a Literature Review
Machine reading comprehension aims to teach machines to understand a text like a human and is a new challenging direction in Artificial Intelligence.
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
We introduce a new English reading comprehension benchmark, DROP, which requires Discrete Reasoning Over the content of Paragraphs.
Ranked #12 on
Question Answering
on DROP Test
Toward Fast and Accurate Neural Discourse Segmentation
Discourse segmentation, which segments texts into Elementary Discourse Units, is a fundamental step in discourse analysis.
Bag-of-Words as Target for Neural Machine Translation
However, most of the existing neural machine translation models only use one of the correct translations as the targets, and the other correct sentences are punished as the incorrect sentences in the training stage.
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification
Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine.
Ranked #3 on
Question Answering
on MS MARCO
Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse Relation Classification
Identifying implicit discourse relations between text spans is a challenging task because it requires understanding the meaning of the text.
General Classification
Implicit Discourse Relation Classification
+2
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
Experiments show that human performance is well above current state-of-the-art baseline systems, leaving plenty of room for the community to make improvements.
A Two-Stage Parsing Method for Text-Level Discourse Analysis
Previous work introduced transition-based algorithms to form a unified architecture of parsing rhetorical structures (including span, nuclearity and relation), but did not achieve satisfactory performance.
Ranked #5 on
Discourse Parsing
on RST-DT
Towards Non-projective High-Order Dependency Parser
This paper presents a novel high-order dependency parsing framework that targets non-projective treebanks.
