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Found 26 papers, 3 papers with code
Nested Named Entity Recognition for Chinese Electronic Health Records with QA-based Sequence Labeling
This proposed QASL approach parallelly asks a corresponding natural language question for each specific named entity type, and then identifies those associated NEs of the same specified type with the BIO tagging scheme.
Mining Commonsense and Domain Knowledge from Math Word Problems
We construct two math datasets and show the effectiveness of our algorithms that they can retrieve the required knowledge for problem-solving.
A Flexible and Extensible Framework for Multiple Answer Modes Question Answering
Only a few of them adopt several answer generation modules for providing different mechanisms; however, they either lack an aggregation mechanism to merge the answers from various modules, or are too complicated to be implemented with neural networks.
Is Character Trigram Overlapping Ratio Still the Best Similarity Measure for Aligning Sentences in a Paraphrased Corpus?
This paper studies if the character trigram is still a suitable similarity measure for the task of aligning sentences in a paragraph paraphrasing corpus.
Chinese Movie Dialogue Question Answering Dataset
This paper also provides a baseline system and shows its performance on this dataset.
GlobalNER: Incorporating Non-local Information into Named Entity Recognition
Nowadays, many Natural Language Processing (NLP) tasks see the demand for incorporating knowledge external to the local information to further improve the performance.
A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers
We present ASDiv (Academia Sinica Diverse MWP Dataset), a diverse (in terms of both language patterns and problem types) English math word problem (MWP) corpus for evaluating the capability of various MWP solvers.
Answering Chinese Elementary School Social Study Multiple Choice Questions
We present a novel approach to answer the Chinese elementary school Social Study Multiple Choice questions.
Sequence to General Tree: Knowledge-Guided Geometry Word Problem Solving
With the recent advancements in deep learning, neural solvers have gained promising results in solving math word problems.
How Fast can BERT Learn Simple Natural Language Inference?
This paper empirically studies whether BERT can really learn to conduct natural language inference (NLI) without utilizing hidden dataset bias; and how efficiently it can learn if it could.
Adopting the Word-Pair-Dependency-Triplets with Individual Comparison for Natural Language Inference
This paper proposes to perform natural language inference with Word-Pair-Dependency-Triplets.
A Meaning-based Statistical English Math Word Problem Solver
We introduce MeSys, a meaning-based approach, for solving English math word problems (MWPs) via understanding and reasoning in this paper.
A Meaning-based English Math Word Problem Solver with Understanding, Reasoning and Explanation
This paper presents a meaning-based statistical math word problem (MWP) solver with understanding, reasoning and explanation.
Building A Case-based Semantic English-Chinese Parallel Treebank
This Treebank is a part of a semantic corpus building project, which aims to build a semantic bilingual corpus annotated with syntactic, semantic cases and word senses.
