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Found 70 papers, 34 papers with code
Continual Pre-training of Language Models for Math Problem Understanding with Syntax-Aware Memory Network
In this paper, we study how to continually pre-train language models for improving the understanding of math problems.
MiMu: Mitigating Multiple Shortcut Learning Behavior of Transformers
Then, we further design self-improvement strategy in target model to reduce the reliance on multiple shortcuts.
NLP-AKG: Few-Shot Construction of NLP Academic Knowledge Graph Based on LLM
Using a few-shot knowledge graph construction method based on LLM, we develop NLP-AKG, an academic knowledge graph for the NLP domain, by extracting 620, 353 entities and 2, 271, 584 relations from 60, 826 papers in ACL Anthology.
Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering
Large language models (LLMs) have shown remarkable capabilities in natural language processing.
StyleBlend: Enhancing Style-Specific Content Creation in Text-to-Image Diffusion Models
Synthesizing visually impressive images that seamlessly align both text prompts and specific artistic styles remains a significant challenge in Text-to-Image (T2I) diffusion models.
MIH-TCCT: Mitigating Inconsistent Hallucinations in LLMs via Event-Driven Text-Code Cyclic Training
Recent methodologies utilizing synthetic datasets have aimed to address inconsistent hallucinations in large language models (LLMs); however, these approaches are primarily tailored to specific tasks, limiting their generalizability.
Optimizing Student Ability Assessment: A Hierarchy Constraint-Aware Cognitive Diagnosis Framework for Educational Contexts
To address this, we propose the Hierarchy Constraint-Aware Cognitive Diagnosis Framework (HCD), designed to more accurately represent student ability performance within real educational contexts.
CursorCore: Assist Programming through Aligning Anything
In this work, we propose a new conversational framework that comprehensively integrates these information sources, collect data to train our models and evaluate their performance.
End-to-End Graph Flattening Method for Large Language Models
In recent years, the breakthrough of Large Language Models (LLMs) offers new ideas for achieving universal methods on graph data.
ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models
To this end, we propose \textbf{\textit{ChemEval}}, which provides a comprehensive assessment of the capabilities of LLMs across a wide range of chemical domain tasks.
Item-Difficulty-Aware Learning Path Recommendation: From a Real Walking Perspective
Learning path recommendation aims to provide learners with a reasonable order of items to achieve their learning goals.
SparkRA: A Retrieval-Augmented Knowledge Service System Based on Spark Large Language Model
Large language models (LLMs) have shown remarkable achievements across various language tasks. To enhance the performance of LLMs in scientific literature services, we developed the scientific literature LLM (SciLit-LLM) through pre-training and supervised fine-tuning on scientific literature, building upon the iFLYTEK Spark LLM.
A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions
Cognitive diagnosis has been developed for decades as an effective measurement tool to evaluate human cognitive status such as ability level and knowledge mastery.
JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models
We leverage it to synthesize 6 million math problems for pre-training our JiuZhang3. 0 model, which only needs to invoke GPT-4 API 9. 3k times and pre-train on 4. 6B data.
Learning to Solve Geometry Problems via Simulating Human Dual-Reasoning Process
Knowledge System controls an implicit reasoning process, which is responsible for providing diagram information and geometry knowledge according to a step-wise reasoning goal generated by Inference System.
Survey of Computerized Adaptive Testing: A Machine Learning Perspective
Computerized Adaptive Testing (CAT) provides an efficient and tailored method for assessing the proficiency of examinees, by dynamically adjusting test questions based on their performance.
Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing
In this paper, we propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method, specifically devised for the distillation of semantic hashing models.
Unified Uncertainty Estimation for Cognitive Diagnosis Models
Specifically, based on the idea of estimating the posterior distributions of cognitive diagnosis model parameters, we first provide a unified objective function for mini-batch based optimization that can be more efficiently applied to a wide range of models and large datasets.
Generative Input: Towards Next-Generation Input Methods Paradigm
The results demonstrate that we have achieved state-of-the-art performance for the first time in the Full-mode Key-sequence to Characters(FK2C) task.
A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations
With the extracted face sequences, we propose a multimodal facial expression-aware emotion recognition model, which leverages the frame-level facial emotion distributions to help improve utterance-level emotion recognition based on multi-task learning.
Ranked #14 on
Emotion Recognition in Conversation
on MELD
Emotion Recognition in Conversation
Facial Expression Recognition (FER)
+2
IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning
IDOL achieves state-of-the-art performance on ReClor and LogiQA, the two most representative benchmarks in logical reasoning MRC, and is proven to be capable of generalizing to different pre-trained models and other types of MRC benchmarks like RACE and SQuAD 2. 0 while keeping competitive general language understanding ability through testing on tasks in GLUE.
Ranked #1 on
Reading Comprehension
on ReClor
JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for Multi-task Mathematical Problem Solving
Specially, we construct a Mixture-of-Experts~(MoE) architecture for modeling mathematical text, so as to capture the common mathematical knowledge across tasks.
From Static Benchmarks to Adaptive Testing: Psychometrics in AI Evaluation
As AI systems continue to grow, particularly generative models like Large Language Models (LLMs), their rigorous evaluation is crucial for development and deployment.
Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning
Based on this finding, we propose a new approach that can deliberate the reasoning steps with tool interfaces, namely \textbf{DELI}.
CSED: A Chinese Semantic Error Diagnosis Corpus
Our experiments show that powerful pre-trained models perform poorly on this corpus.
MiniRBT: A Two-stage Distilled Small Chinese Pre-trained Model
In natural language processing, pre-trained language models have become essential infrastructures.
Towards a Holistic Understanding of Mathematical Questions with Contrastive Pre-training
To this end, in this paper, we propose a novel contrastive pre-training approach for mathematical question representations, namely QuesCo, which attempts to bring questions with more similar purposes closer.
Gradient-based Intra-attention Pruning on Pre-trained Language Models
In this work, we propose a structured pruning method GRAIN (Gradient-based Intra-attention pruning), which performs task-specific pruning with knowledge distillation and yields highly effective models.
LERT: A Linguistically-motivated Pre-trained Language Model
We propose LERT, a pre-trained language model that is trained on three types of linguistic features along with the original MLM pre-training task, using a linguistically-informed pre-training (LIP) strategy.
Ranked #6 on
Stock Market Prediction
on Astock
Overview of CTC 2021: Chinese Text Correction for Native Speakers
In this paper, we present an overview of the CTC 2021, a Chinese text correction task for native speakers.
JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem Understanding
Considering the complex nature of mathematical texts, we design a novel curriculum pre-training approach for improving the learning of mathematical PLMs, consisting of both basic and advanced courses.
Cross-Lingual Text Classification with Multilingual Distillation and Zero-Shot-Aware Training
In this paper, we aim to improve the multilingual model's supervised and zero-shot performance simultaneously only with the resources from supervised languages.
InterHT: Knowledge Graph Embeddings by Interaction between Head and Tail Entities
Knowledge graph embedding (KGE) models learn the representation of entities and relations in knowledge graphs.
Ranked #4 on
Link Property Prediction
on ogbl-wikikg2
Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models
Achieving human-level performance on some of the Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs).
IFlyEA: A Chinese Essay Assessment System with Automated Rating, Review Generation, and Recommendation
IFlyEA provides multi-level and multi-dimension analytical modules for essay assessment.
Adversarial Training for Machine Reading Comprehension with Virtual Embeddings
Adversarial training (AT) as a regularization method has proved its effectiveness on various tasks.
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual Transfer
Multilingual pre-trained models have achieved remarkable performance on cross-lingual transfer learning.
ExpMRC: Explainability Evaluation for Machine Reading Comprehension
Achieving human-level performance on some of Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs).
Ranked #1 on
Multi-Choice MRC
on ExpMRC - RACE+ (test)
Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering
To deal with this challenge, most of the existing works consider paragraphs as nodes in a graph and propose graph-based methods to retrieve them.
Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing
In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models.
Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer's Disease Detection
Finally, a best detection accuracy of 81. 6{\%} is obtained by our proposed methods on the Mandarin AD corpus.
Unsupervised Explanation Generation for Machine Reading Comprehension
With the blooming of various Pre-trained Language Models (PLMs), Machine Reading Comprehension (MRC) has embraced significant improvements on various benchmarks and even surpass human performances.
CharBERT: Character-aware Pre-trained Language Model
Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almost avoidable.
Combining ResNet and Transformer for Chinese Grammatical Error Diagnosis
Grammatical error diagnosis is an important task in natural language processing.
Revisiting Pre-Trained Models for Chinese Natural Language Processing
Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pre-trained language models.
Ranked #13 on
Stock Market Prediction
on Astock
Benchmarking Robustness of Machine Reading Comprehension Models
To fill this important gap, we construct AdvRACE (Adversarial RACE), a new model-agnostic benchmark for evaluating the robustness of MRC models under four different types of adversarial attacks, including our novel distractor extraction and generation attacks.
Conversational Word Embedding for Retrieval-Based Dialog System
Human conversations contain many types of information, e. g., knowledge, common sense, and language habits.
A Sentence Cloze Dataset for Chinese Machine Reading Comprehension
To add diversity in this area, in this paper, we propose a new task called Sentence Cloze-style Machine Reading Comprehension (SC-MRC).
Is Graph Structure Necessary for Multi-hop Question Answering?
We construct a strong baseline model to establish that, with the proper use of pre-trained models, graph structure may not be necessary for multi-hop question answering.
TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing
In this paper, we introduce TextBrewer, an open-source knowledge distillation toolkit designed for natural language processing.
CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension
We present a Chinese judicial reading comprehension (CJRC) dataset which contains approximately 10K documents and almost 50K questions with answers.
Discriminative Sentence Modeling for Story Ending Prediction
Story Ending Prediction is a task that needs to select an appropriate ending for the given story, which requires the machine to understand the story and sometimes needs commonsense knowledge.
Contextual Recurrent Units for Cloze-style Reading Comprehension
Recurrent Neural Networks (RNN) are known as powerful models for handling sequential data, and especially widely utilized in various natural language processing tasks.
Improving Machine Reading Comprehension via Adversarial Training
With virtual adversarial training (VAT), we explore the possibility of improving the RC models with semi-supervised learning and prove that examples from a different task are also beneficial.
IFlyLegal: A Chinese Legal System for Consultation, Law Searching, and Document Analysis
To our knowledge, IFlyLegal is the first Chinese legal system that employs up-to-date NLP techniques and caters for needs of different user groups, such as lawyers, judges, procurators, and clients.
TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-based Chatbots
The heart of TripleNet is a novel attention mechanism named triple attention to model the relationships within the triple at four levels.
Cross-Lingual Machine Reading Comprehension
In this paper, we propose Cross-Lingual Machine Reading Comprehension (CLMRC) task for the languages other than English.
Neural Cognitive Diagnosis for Intelligent Education Systems
Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts.
QuesNet: A Unified Representation for Heterogeneous Test Questions
Then we propose a two-level hierarchical pre-training algorithm to learn better understanding of test questions in an unsupervised way.
Exploiting Cognitive Structure for Adaptive Learning
Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e. g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items.
Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions
Machine Reading Comprehension (MRC) with multiple-choice questions requires the machine to read given passage and select the correct answer among several candidates.
A Span-Extraction Dataset for Chinese Machine Reading Comprehension
Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention.
Chinese Grammatical Error Diagnosis using Statistical and Prior Knowledge driven Features with Probabilistic Ensemble Enhancement
This paper describes our system at NLPTEA-2018 Task {\#}1: Chinese Grammatical Error Diagnosis.
HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for Commonsense Reading Comprehension
This paper describes the system which got the state-of-the-art results at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge.
Dataset for the First Evaluation on Chinese Machine Reading Comprehension
Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention.
Attention-over-Attention Neural Networks for Reading Comprehension
Cloze-style queries are representative problems in reading comprehension.
Ranked #3 on
Question Answering
on Children's Book Test
Consensus Attention-based Neural Networks for Chinese Reading Comprehension
Reading comprehension has embraced a booming in recent NLP research.
Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution
Most existing approaches for zero pronoun resolution are heavily relying on annotated data, which is often released by shared task organizers.
LSTM Neural Reordering Feature for Statistical Machine Translation
Artificial neural networks are powerful models, which have been widely applied into many aspects of machine translation, such as language modeling and translation modeling.
