Search Results for author:
Found 13 papers, 9 papers with code
ReFT: Reasoning with Reinforced Fine-Tuning
ReFT first warmups the model with SFT, and then employs on-line reinforcement learning, specifically the PPO algorithm in this paper, to further fine-tune the model, where an abundance of reasoning paths are automatically sampled given the question and the rewards are naturally derived from the ground-truth answers.
Design of Chain-of-Thought in Math Problem Solving
We also find that Python is a better choice of language than Wolfram for program CoTs.
Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning
To address these issues, we investigate two approaches to leverage the training data in a few-shot prompting scenario: dynamic program prompting and program distillation.
Sequence-to-Sequence Pre-training with Unified Modality Masking for Visual Document Understanding
This paper presents GenDoc, a general sequence-to-sequence document understanding model pre-trained with unified masking across three modalities: text, image, and layout.
document understanding
Optical Character Recognition (OCR)
+1
Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction
Solving math word problems requires deductive reasoning over the quantities in the text.
Ranked #4 on
Math Word Problem Solving
on MathQA
To be Closer: Learning to Link up Aspects with Opinions
Dependency parse trees are helpful for discovering the opinion words in aspect-based sentiment analysis (ABSA).
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Better Feature Integration for Named Entity Recognition
We believe this is because both types of features - the contextual information captured by the linear sequences and the structured information captured by the dependency trees may complement each other.
ENT-DESC: Entity Description Generation by Exploring Knowledge Graph
Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description.
Ranked #1 on
KG-to-Text Generation
on ENT-DESC
Dependency-Guided LSTM-CRF for Named Entity Recognition
Dependency tree structures capture long-distance and syntactic relationships between words in a sentence.
Ranked #1 on
Chinese Named Entity Recognition
on OntoNotes 5.0
Chinese Named Entity Recognition
named-entity-recognition
+2
Better Modeling of Incomplete Annotations for Named Entity Recognition
Supervised approaches to named entity recognition (NER) are largely developed based on the assumption that the training data is fully annotated with named entity information.
Efficient Dependency-Guided Named Entity Recognition
It has been shown previously that such information can be used to improve the performance of NER (Sasano and Kurohashi 2008, Ling and Weld 2012).
Dependency-based Hybrid Trees for Semantic Parsing
We propose a novel dependency-based hybrid tree model for semantic parsing, which converts natural language utterance into machine interpretable meaning representations.
