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Found 43 papers, 9 papers with code
Complicate Then Simplify: A Novel Way to Explore Pre-trained Models for Text Classification
However such way might not fully explore the knowledge in PTMs as it is constrained by the difficulty of the task.
A Novel Framework Based on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge
Medical code prediction from clinical notes aims at automatically associating medical codes with the clinical notes.
Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge
Recent years have witnessed growing interests in incorporating external knowledge such as pre-trained word embeddings (PWEs) or pre-trained language models (PLMs) into neural topic modeling.
Beyond Text: Incorporating Metadata and Label Structure for Multi-Label Document Classification using Heterogeneous Graphs
The other is label heterogeneous graph, which is constructed based on both the labels’ hierarchy and their statistical dependencies.
Implicit Sentiment Analysis with Event-centered Text Representation
Implicit sentiment analysis, aiming at detecting the sentiment of a sentence without sentiment words, has become an attractive research topic in recent years.
A Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation
Therefore, in this paper, multi-hop relation detection is considered as a multi-label learning problem.
Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding
Due to their incompleteness, a fundamental task for KGs, which is known as Knowledge Graph Completion (KGC), is to perform link prediction and infer new facts based on the known facts.
A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering
However, most of existing approaches only detect one single path to obtain the answer without considering other correct paths, which might affect the final performance.
Large, Small or Both: A Novel Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization
In specific, a small size of synthesized negative reviews is obtained by rewriting the positive text via a large language model.
Fine-grainedly Synthesize Streaming Data Based On Large Language Models With Graph Structure Understanding For Data Sparsity
Due to the sparsity of user data, sentiment analysis on user reviews in e-commerce platforms often suffers from poor performance, especially when faced with extremely sparse user data or long-tail labels.
Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door Adjustment
Conventional multi-hop fact verification models are prone to rely on spurious correlations from the annotation artifacts, leading to an obvious performance decline on unbiased datasets.
Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment
In specific, causal intervention is implemented by designing the prompts without accessing the parameters and logits of LLMs. The chain-of-thoughts generated by LLMs are employed as the mediator variable and the causal effect between the input prompt and the output answers is calculated through front-door adjustment to mitigate model biases.
STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models
For poor model calibration, we incorporate the regularization method during LoRA training to keep the model from being over-confident, and the Monte-Carlo dropout mechanism is employed to enhance the uncertainty estimation.
DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal Inference
However, most of the present debiasing methods focus on single-variable causal inference, which is not suitable for ABSA with two input variables (the target aspect and the review).
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Computational Experiments Meet Large Language Model Based Agents: A Survey and Perspective
These agents, known as LLM-based Agent, offer the potential to enhance the anthropomorphism lacking in ABM.
EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification
Experimental results show that the proposed approach outperforms the SOTA baselines and can generate linguistically diverse counterfactual data without disrupting their logical relationships.
Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources
Specifically, with the use of the neural topic model and the language model, the target information is augmented by explainable topic representations.
Feature Representation Learning for NL2SQL Generation Based on Coupling and Decoupling
To address this issue, we propose the Clause Feature Correlation Decoupling and Coupling (CFCDC) model, which uses a feature representation decoupling method to separate the SELECT and WHERE clauses at the parameter level.
Consistent Multi-Granular Rationale Extraction for Explainable Multi-hop Fact Verification
The success of deep learning models on multi-hop fact verification has prompted researchers to understand the behavior behind their veracity.
Talking Head Generation with Probabilistic Audio-to-Visual Diffusion Priors
In this paper, we introduce a simple and novel framework for one-shot audio-driven talking head generation.
Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning
In specific, GCN is utilized to incorporate the topological interaction information among multiple pieces of evidence for learning evidence representation.
SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition
Current few-shot NER methods focus on leveraging existing datasets in the rich-resource domains which might fail in a training-from-scratch setting where no source-domain data is used.
Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification
To alleviate the above issues, we propose a novel topic-aware evidence reasoning and stance-aware aggregation model for more accurate fact verification, with the following four key properties: 1) checking topical consistency between the claim and evidence; 2) maintaining topical coherence among multiple pieces of evidence; 3) ensuring semantic similarity between the global topic information and the semantic representation of evidence; 4) aggregating evidence based on their implicit stances to the claim.
Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection
Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intricate transition patterns between the affective states.
Ranked #12 on
Emotion Recognition in Conversation
on DailyDialog
Variational Gaussian Topic Model with Invertible Neural Projections
Based on the variational auto-encoder, the proposed VaGTM models each topic with a multivariate Gaussian in decoder to incorporate word relatedness.
Emotion Classification by Jointly Learning to Lexiconize and Classify
Emotion lexicons have been shown effective for emotion classification (Baziotis et al., 2018).
Neural Topic Modeling with Cycle-Consistent Adversarial Training
ToMCAT employs a generator network to interpret topics and an encoder network to infer document topics.
Neural Topic Modeling by Incorporating Document Relationship Graph
Graph Neural Networks (GNNs) that capture the relationships between graph nodes via message passing have been a hot research direction in the natural language processing community.
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings
We propose a novel generative model to explore both local and global context for joint learning topics and topic-specific word embeddings.
Neural Temporal Opinion Modelling for Opinion Prediction on Twitter
Opinion prediction on Twitter is challenging due to the transient nature of tweet content and neighbourhood context.
Neural Topic Modeling with Bidirectional Adversarial Training
Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA).
Ranked #1 on
Text Clustering
on 20 Newsgroups
Interpretable Relevant Emotion Ranking with Event-Driven Attention
Unveiling the hidden event information can help to understand how the emotions are evoked and provide explainable results.
Variational Conditional GAN for Fine-grained Controllable Image Generation
In this paper, we propose a novel variational generator framework for conditional GANs to catch semantic details for improving the generation quality and diversity.
Open Event Extraction from Online Text using a Generative Adversarial Network
Experimental results show that our model outperforms the baseline approaches on all the datasets, with more significant improvements observed on the news article dataset where an increase of 15\% is observed in F-measure.
ATM:Adversarial-neural Topic Model
The proposed ATM models topics with Dirichlet prior and employs a generator network to capture the semantic patterns among latent topics.
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking
As such, it is crucial to predict and rank multiple relevant emotions by their intensities.
Relevant Emotion Ranking from Text Constrained with Emotion Relationships
As such, emotion detection, to predict multiple emotions associated with a given text, can be cast into a multi-label classification problem.
Neural Storyline Extraction Model for Storyline Generation from News Articles
To tackle this problem, approaches based on probabilistic graphic models jointly model the generations of events and storylines without the use of annotated data.
Event extraction from Twitter using Non-Parametric Bayesian Mixture Model with Word Embeddings
To extract structured representations of newsworthy events from Twitter, unsupervised models typically assume that tweets involving the same named entities and expressed using similar words are likely to belong to the same event.
