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Found 15 papers, 6 papers with code
Frustratingly Easy System Combination for Grammatical Error Correction
In this paper, we formulate system combination for grammatical error correction (GEC) as a simple machine learning task: binary classification.
Ranked #2 on
Grammatical Error Correction
on BEA-2019 (test)
SISER: Semantic-Infused Selective Graph Reasoning for Fact Verification
This study proposes Semantic-Infused SElective Graph Reasoning (SISER) for fact verification, which newly presents semantic-level graph reasoning and injects its reasoning-enhanced representation into other types of graph-based and sequence-based reasoning methods.
JBNU-CCLab at SemEval-2022 Task 7: DeBERTa for Identifying Plausible Clarifications in Instructional Texts
In this study, we examine the ability of contextualized representations of pretrained language model to distinguish whether sequences from instructional articles are plausible or implausible.
JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their Descriptions
This paper describes our system in the SemEval-2022 Task 12: ‘linking mathematical symbols to their descriptions’, achieving first on the leaderboard for all the subtasks comprising named entity extraction (NER) and relation extraction (RE).
Joint Entity and Relation Extraction
Machine Reading Comprehension
+1
MAFiD: Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data
Transformer-based models for question answering (QA) over tables and texts confront a “long” hybrid sequence over tabular and textual elements, causing long-range reasoning problems.
Ranked #1 on
Question Answering
on HybridQA
Feature Structure Distillation with Centered Kernel Alignment in BERT Transferring
To resolve it in transferring, we investigate distillation of structures of representations specified to three types: intra-feature, local inter-feature, global inter-feature structures.
Impact of representation matching with neural machine translation
Most neural machine translation models are implemented as a conditional language model framework composed of encoder and decoder models.
JBNU at SemEval-2020 Task 4: BERT and UniLM for Commonsense Validation and Explanation
This paper presents our contributions to the SemEval-2020 Task 4 Commonsense Validation and Explanation (ComVE) and includes the experimental results of the two Subtasks B and C of the SemEval-2020 Task 4.
JBNU at MRP 2020: AMR Parsing Using a Joint State Model for Graph-Sequence Iterative Inference
This paper describes the Jeonbuk National University (JBNU) system for the 2020 shared task on Cross-Framework Meaning Representation Parsing at the Conference on Computational Natural Language Learning.
JBNU at MRP 2019: Multi-level Biaffine Attention for Semantic Dependency Parsing
We propose a unified parsing model using biaffine attention (Dozat and Manning, 2017), consisting of 1) a BERT-BiLSTM encoder and 2) a biaffine attention decoder.
QE BERT: Bilingual BERT Using Multi-task Learning for Neural Quality Estimation
Our proposed model is re-purposed BERT for the translation quality estimation and uses multi-task learning for the sentence-level task and word-level subtasks (i. e., source word, target word, and target gap).
Concept Equalization to Guide Correct Training of Neural Machine Translation
Neural machine translation decoders are usually conditional language models to sequentially generate words for target sentences.
Improving Term Frequency Normalization for Multi-topical Documents, and Application to Language Modeling Approaches
Although these document characteristics should be differently handled, all previous methods of term frequency normalization have ignored these differences and have used a simplified length-driven approach which decreases the term frequency by only the length of a document, causing an unreasonable penalization.
