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Found 44 papers, 13 papers with code
Position Offset Label Prediction for Grammatical Error Correction
We introduce a novel position offset label prediction subtask to the encoder-decoder architecture for grammatical error correction (GEC) task.
Focus-Driven Contrastive Learning for Medical Question Summarization
Automatic medical question summarization can significantly help the system to understand consumer health questions and retrieve correct answers.
Prompting Large Language Models for Zero-shot Essay Scoring via Multi-trait Specialization
Then, an LLM is prompted to extract trait scores from several conversational rounds, each round scoring one of the traits based on the scoring criteria.
FPT: Feature Prompt Tuning for Few-shot Readability Assessment
Our proposed method establishes a new architecture for prompt tuning that sheds light on how linguistic features can be easily adapted to linguistic-related tasks.
Going Beyond Word Matching: Syntax Improves In-context Example Selection for Machine Translation
In-context learning (ICL) is the trending prompting strategy in the era of large language models (LLMs), where a few examples are demonstrated to evoke LLMs' power for a given task.
Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction
In this paper, we propose a novel ungrammatical-syntax-based in-context example selection strategy for GEC.
Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding
To address them, we propose Mixture-of-Prompt-Experts with Block-Aware Prompt Fusion (MoPE-BAF), a novel multi-modal soft prompt framework based on the unified vision-language model (VLM).
Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment
Multi-modal semantic understanding requires integrating information from different modalities to extract users' real intention behind words.
Evaluating the Capability of Large-scale Language Models on Chinese Grammatical Error Correction Task
Large-scale language models (LLMs) has shown remarkable capability in various of Natural Language Processing (NLP) tasks and attracted lots of attention recently.
Are Pre-trained Language Models Useful for Model Ensemble in Chinese Grammatical Error Correction?
To this end, we explore several ensemble strategies based on strong PLMs with four sophisticated single models.
An Error-Guided Correction Model for Chinese Spelling Error Correction
By borrowing the powerful ability of BERT, we propose a novel zero-shot error detection method to do a preliminary detection, which guides our model to attend more on the probably wrong tokens in encoding and to avoid modifying the correct tokens in generating.
From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction
To handle grammatical error correction, we design part-of-speech (POS) features and semantic class features to enhance the neural network model, and propose an auxiliary task to predict the POS sequence of the target sentence.
A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Loss
For readability assessment, traditional methods mainly employ machine learning classifiers with hundreds of linguistic features.
Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation
Specially, we present localness modeling with a Gaussian bias to enable the model to focus on answer-surrounded context, and propose a mask attention mechanism to make the syntactic structure of input passage accessible in question generation process.
Ranked #5 on
Question Generation
on SQuAD1.1
Focus-Driven Contrastive Learniang for Medical Question Summarization
Automatic medical question summarization can significantly help the system to understand consumer health questions and retrieve correct answers.
Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained Models
To strengthen the word boundary information, we mix the representations of the internal characters within a word.
Well-classified Examples are Underestimated in Classification with Deep Neural Networks
The conventional wisdom behind learning deep classification models is to focus on bad-classified examples and ignore well-classified examples that are far from the decision boundary.
Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data
This paper for the first time addresses the question-answer pair generation task on the real-world examination data, and proposes a new unified framework on RACE.
ASAT: Adaptively Scaled Adversarial Training in Time Series
Besides the security concerns of potential adversarial examples, adversarial training can also improve the generalization ability of neural networks, train robust neural networks, and provide interpretability for neural networks.
Enhancing Question Generation with Commonsense Knowledge
Question generation (QG) is to generate natural and grammatical questions that can be answered by a specific answer for a given context.
Alleviating the Knowledge-Language Inconsistency: A Study for Deep Commonsense Knowledge
Knowledge facts are typically represented by relational triples, while we observe that some commonsense facts are represented by the triples whose forms are inconsistent with the expression of language.
EQG-RACE: Examination-Type Question Generation
Question Generation (QG) is an essential component of the automatic intelligent tutoring systems, which aims to generate high-quality questions for facilitating the reading practice and assessments.
Exploiting WordNet Synset and Hypernym Representations for Answer Selection
Answer selection (AS) is an important subtask of document-based question answering (DQA).
Knowledge-Aware Procedural Text Understanding with Multi-Stage Training
In this work, we focus on the task of procedural text understanding, which aims to comprehend such documents and track entities' states and locations during a process.
How to Ask Good Questions? Try to Leverage Paraphrases
Given a sentence and its relevant answer, how to ask good questions is a challenging task, which has many real applications.
A Question Type Driven and Copy Loss Enhanced Frameworkfor Answer-Agnostic Neural Question Generation
The answer-agnostic question generation is a significant and challenging task, which aims to automatically generate questions for a given sentence but without an answer.
Query-Variant Advertisement Text Generation with Association Knowledge
In this paper, we propose the query-variant advertisement text generation task that aims to generate candidate advertisement texts for different web search queries with various needs based on queries and item keywords.
Jointly Modeling Aspect and Sentiment with Dynamic Heterogeneous Graph Neural Networks
Target-Based Sentiment Analysis aims to detect the opinion aspects (aspect extraction) and the sentiment polarities (sentiment detection) towards them.
Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension
Second, they didn't emphasize the relationship between the distractor and article, making the generated distractors not semantically relevant to the article and thus fail to form a set of meaningful options.
Multi-level Gated Recurrent Neural Network for Dialog Act Classification
In this paper we focus on the problem of dialog act (DA) labelling.
Question-type Driven Question Generation
Question generation is a challenging task which aims to ask a question based on an answer and relevant context.
Multi-Task Learning with Language Modeling for Question Generation
This paper explores the task of answer-aware questions generation.
Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model
In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph.
Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model
In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph.
A Simple Dual-decoder Model for Generating Response with Sentiment
How to generate human like response is one of the most challenging tasks for artificial intelligence.
Learning Universal Sentence Representations with Mean-Max Attention Autoencoder
In the encoding we propose a mean-max strategy that applies both mean and max pooling operations over the hidden vectors to capture diverse information of the input.
Sememe Prediction: Learning Semantic Knowledge from Unstructured Textual Wiki Descriptions
In the experiments, we take a real-world sememe knowledge base HowNet and the corresponding descriptions of the words in Baidu Wiki for training and evaluation.
An Unsupervised Model with Attention Autoencoders for Question Retrieval
In this paper, we propose a novel unsupervised framework, namely reduced attentive matching network (RAMN), to compute semantic matching between two questions.
Improving Word Vector with Prior Knowledge in Semantic Dictionary
Using low dimensional vector space to represent words has been very effective in many NLP tasks.
Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering
In this paper, we focus on the problem of answer triggering ad-dressed by Yang et al. (2015), which is a critical component for a real-world question answering system.
