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- CodeBERT: A Pre-Trained Model for Programming and Natural Languages4,421
This work develops CodeBERT with Transformer-based neural architecture, and trains it with a hybrid objective function that incorporates the pre-training task of replaced token detection, which is to detect plausible alternatives sampled from generators.
- A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions3,965
A thorough overview of hallucination detection methods and benchmarks is presented and the promising research directions on LLM hallucinations are highlighted, including hallucination in large vision-language models and understanding of knowledge boundaries in LLM hallucinations.
- SemEval-2016 Task 5: Aspect Based Sentiment Analysis1,740
This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015, which attracted 245 submissions from 29 teams and provided 19 training and 20 testing datasets.
- Document Modeling with Gated Recurrent Neural Network for Sentiment Classification1,547
A neural network model is introduced to learn vector-based document representation in a unified, bottom-up fashion and dramatically outperforms standard recurrent neural network in document modeling for sentiment classification.
- Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification1,229
Three neural networks are developed to effectively incorporate the supervision from sentiment polarity of text (e.g. sentences or tweets) in their loss functions and the performance of SSWE is improved by concatenating SSWE with existing feature set.
- Aspect Level Sentiment Classification with Deep Memory Network1,046
A deep memory network for aspect level sentiment classification that explicitly captures the importance of each context word when inferring the sentiment polarity of an aspect, and is also fast.
- Effective LSTMs for Target-Dependent Sentiment Classification1,010
Two target dependent long short-term memory models, where target information is automatically taken into account, are developed, which achieve state-of-the-art performances without using syntactic parser or external sentiment lexicons.
- Learning Semantic Representations of Users and Products for Document Level Sentiment Classification378
By combining evidence at user-, product and documentlevel in a unified neural framework, the proposed model achieves state-of-the-art performances on IMDB and Yelp datasets1.
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- HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge311
The experimental results demonstrate that HuaTuo generates responses that possess more reliable medical knowledge, and a LLaMA-based model that has been supervised-fine-tuned with generated QA (Question-Answer) instances is proposed.
- Learning Semantic Hierarchies via Word Embeddings289
This paper proposes a novel and effective method for the construction of semantic hierarchies based on word embeddings, which can be used to measure the semantic relationship between words.
- A language-independent neural network for event detection282
A language-independent neural network is developed to capture both sequence and chunk information from specific contexts and use them to train an event detector for multiple languages without any manually encoded features.
- Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future280
This paper systematically investigate relevant research, summarizing advanced methods through a meticulous taxonomy that offers novel perspectives, and delve into the current frontiers and delineate the challenges and future directions, thereby shedding light on future research.
- Coooolll: A Deep Learning System for Twitter Sentiment Classification251
A neural network with hybrid loss function 1 is developed to learn SSWE, which encodes the sentiment information of tweets in the continuous representation of words, which can be easily re-implemented with the publicly available sentiment-specific word embedding.
- Deep learning for sentiment analysis: successful approaches and future challenges207
An overview of the successful deep learning approaches for sentiment analysis tasks is provided, lay out the remaining challenges and provide some suggestions to address these challenges.
- Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach192
The sentiment lexicon (TS-Lex) is built with a dedicated neural architecture and integrates the sentiment information of text into its hybrid loss function for learning sentiment-specific phrase embedding (SSPE).
- Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate171
This work introduces a formal debate framework (FORD) to conduct a three-stage debate among LLMs with real-world scenarios alignment: fair debate, mismatched debate, and roundtable debate to examine whether LLMs can collaborate effectively to achieve a consensus for a shared goal.
- User modeling with neural network for review rating prediction163
The lexical semantic composition models are extended and a userword composition vector model (UWCVM) is introduced, which effectively captures how user acts as a function affecting the continuous word representation.
- Document representation and feature combination for deceptive spam review detection154
A neural network based model is presented to learn the representation of reviews and makes a hard attention through the composition from sentence representation into document representation, compute the importance weights of each sentence and incorporate them into the composition process of document representation.
- A Text-Centered Shared-Private Framework via Cross-Modal Prediction for Multimodal Sentiment Analysis151
This paper proposes a text-centered shared-private framework (TCSP) for multimodal fusion, which consists of the cross-modal prediction and sentiment regression parts and provides a new way to utilize the unlabeled data for multimodal sentiment analysis.
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- A Survey on Dialogue Summarization: Recent Advances and New Frontiers127
A thorough review of dialogue summarization according to the characteristics of each domain, covering meeting, chat, email thread, customer service and medical dialogue, as well as an overview of publicly available research datasets.
- Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure125
The Molweni dataset is presented, a machine reading comprehension (MRC) dataset with discourse structure built over multiparty dialog with large-scale annotations in a modified Segmented Discourse Representation Theory (SDRT; Asher et al., 2016) style.
- Effective Deep Memory Networks for Distant Supervised Relation Extraction122
This paper introduces a novel neural approach for distant supervised RE with special focus on attention mechanisms, which includes two major attention-based memory components, which are capable of explicitly capturing the importance of each context word for modeling the representation of the entity pair.
- Target-Dependent Sentiment Classification with Long Short Term Memory.122
Two target dependent long short-term memory models, where target information is automatically taken into account, are developed, which achieve state-of-the-art performances without using syntactic parser or external sentiment lexicons.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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