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Found 44 papers, 29 papers with code
Learning to Answer Psychological Questionnaire for Personality Detection
We argue that the posts created by a user contain critical contents that could help answer the questions in a questionnaire, resulting in an assessment of his personality by linking the texts and the questionnaire.
RoleInteract: Evaluating the Social Interaction of Role-Playing Agents
In this paper, we introduce RoleInteract, the first benchmark designed to systematically evaluate the sociality of role-playing conversational agents at both individual and group levels of social interactions.
FuseChat: Knowledge Fusion of Chat Models
Recently, \textsc{FuseLLM} introduced the concept of knowledge fusion to transfer the collective knowledge of multiple structurally varied LLMs into a target LLM through lightweight continual training.
Alirector: Alignment-Enhanced Chinese Grammatical Error Corrector
Then, we combine the source sentence with the initial correction and feed it through an alignment model for another round of correction, aiming to enforce the alignment model to focus on potential overcorrection.
Knowledge Fusion of Large Language Models
In this paper, we introduce the notion of knowledge fusion for LLMs, aimed at combining the capabilities of existing LLMs and transferring them into a single LLM.
Knowledge Verification to Nip Hallucination in the Bud
While large language models (LLMs) have demonstrated exceptional performance across various tasks following human alignment, they may still generate responses that sound plausible but contradict factual knowledge, a phenomenon known as \emph{hallucination}.
Small LLMs Are Weak Tool Learners: A Multi-LLM Agent
Each component is implemented by a single LLM that focuses on a specific capability and collaborates with others to accomplish the task.
Knowledge Distillation for Closed-Source Language Models
The prior estimation aims to derive a prior distribution by utilizing the corpus generated by closed-source language models, while the posterior estimation employs a proxy model to update the prior distribution and derive a posterior distribution.
PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection
Drawing inspiration from Psychological Questionnaires, which are carefully designed by psychologists to evaluate individual personality traits through a series of targeted items, we argue that these items can be regarded as a collection of well-structured chain-of-thought (CoT) processes.
Dual-Feedback Knowledge Retrieval for Task-Oriented Dialogue Systems
Efficient knowledge retrieval plays a pivotal role in ensuring the success of end-to-end task-oriented dialogue systems by facilitating the selection of relevant information necessary to fulfill user requests.
MCC-KD: Multi-CoT Consistent Knowledge Distillation
Large language models (LLMs) have showcased remarkable capabilities in complex reasoning through chain of thought (CoT) prompting.
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration
Instruction-tuning can be substantially optimized through enhanced diversity, resulting in models capable of handling a broader spectrum of tasks.
Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue System
The results demonstrate that when combined with meta knowledge, the response generator can effectively leverage high-quality knowledge records from the retriever and enhance the quality of generated responses.
Disentangled Phonetic Representation for Chinese Spelling Correction
Chinese Spelling Correction (CSC) aims to detect and correct erroneous characters in Chinese texts.
Clustering-Aware Negative Sampling for Unsupervised Sentence Representation
Contrastive learning has been widely studied in sentence representation learning.
Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog
Retrieving proper domain knowledge from an external database lies at the heart of end-to-end task-oriented dialog systems to generate informative responses.
AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression
To enhance the knowledge transfer of model reasoning and generalization, we further explore multi-view attribution distillation on all potential decisions of the teacher.
Generic Dependency Modeling for Multi-Party Conversation
To model the dependencies between utterances in multi-party conversations, we propose a simple and generic framework based on the dependency parsing results of utterances.
Orders Are Unwanted: Dynamic Deep Graph Convolutional Network for Personality Detection
Predicting personality traits based on online posts has emerged as an important task in many fields such as social network analysis.
AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning
Fine-tuning large pre-trained language models on downstream tasks is apt to suffer from overfitting when limited training data is available.
XPrompt: Exploring the Extreme of Prompt Tuning
Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for performing downstream tasks in a parameter-efficient manner.
Autoregressive Entity Generation for End-to-End Task-Oriented Dialog
In either approach, the systems may generate a response with conflicting entity information.
UBARv2: Towards Mitigating Exposure Bias in Task-Oriented Dialogs
This paper studies the exposure bias problem in task-oriented dialog systems, where the model's generated content over multiple turns drives the dialog context away from the ground-truth distribution at training time, introducing error propagation and damaging the robustness of the TOD system.
Joint Generator-Ranker Learning for Natural Language Generation
Generate-then-rank is a widely used mechanism for text generation, where a generator produces multiple text candidates and a ranker chooses the best one among the text candidates.
GL-RG: Global-Local Representation Granularity for Video Captioning
Video captioning is a challenging task as it needs to accurately transform visual understanding into natural language description.
Deep Partial Multiplex Network Embedding
Network embedding is an effective technique to learn the low-dimensional representations of nodes in networks.
WebFormer: The Web-page Transformer for Structure Information Extraction
Structure information extraction refers to the task of extracting structured text fields from web pages, such as extracting a product offer from a shopping page including product title, description, brand and price.
Psycholinguistic Tripartite Graph Network for Personality Detection
In this paper, we propose a psycholinguistic knowledge-based tripartite graph network, TrigNet, which consists of a tripartite graph network and a BERT-based graph initializer.
Retrieve & Memorize: Dialog Policy Learning with Multi-Action Memory
In this paper, we propose a retrieve-and-memorize framework to enhance the learning of system actions.
Bi-Granularity Contrastive Learning for Post-Training in Few-Shot Scene
The major paradigm of applying a pre-trained language model to downstream tasks is to fine-tune it on labeled task data, which often suffers instability and low performance when the labeled examples are scarce.~One way to alleviate this problem is to apply post-training on unlabeled task data before fine-tuning, adapting the pre-trained model to target domains by contrastive learning that considers either token-level or sequence-level similarity.
Directed Acyclic Graph Network for Conversational Emotion Recognition
In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea.
Ranked #10 on
Emotion Recognition in Conversation
on DailyDialog
Syntax-Enhanced Pre-trained Model
We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa.
DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition
Specifically, we first modify the recurrence mechanism of XLNet from segment-level to utterance-level in order to better model the conversational data.
Ranked #2 on
Emotion Recognition in Conversation
on CPED
UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2
This paper presents our task-oriented dialog system UBAR which models task-oriented dialogs on a dialog session level.
Constituency Lattice Encoding for Aspect Term Extraction
One of the remaining challenges for aspect term extraction in sentiment analysis resides in the extraction of phrase-level aspect terms, which is non-trivial to determine the boundaries of such terms.
Aspect Term Extraction and Sentiment Classification
Sentence
+1
Multi-choice Relational Reasoning for Machine Reading Comprehension
We propose a multi-choice relational reasoning (McR$^2$) model with an aim to enable relational reasoning on candidates based on fusion representations of document, query and candidates.
Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge
Chinese word segmentation (CWS) and part-of-speech (POS) tagging are important fundamental tasks for Chinese language processing, where joint learning of them is an effective one-step solution for both tasks.
Low-Resource Generation of Multi-hop Reasoning Questions
Specifically, we first build a multi-hop generation model and guide it to satisfy the logical rationality by the reasoning chain extracted from a given text.
Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation
In this paper, we formulate the data augmentation as a conditional generation task: generating a new sentence while preserving the original opinion targets and labels.
Multi-Domain Dialogue Acts and Response Co-Generation
Unlike those pipeline approaches, our act generation module preserves the semantic structures of multi-domain dialogue acts and our response generation module dynamically attends to different acts as needed.
Relational Graph Attention Network for Aspect-based Sentiment Analysis
Then, we propose a relational graph attention network (R-GAT) to encode the new tree structure for sentiment prediction.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
A Deep Neural Information Fusion Architecture for Textual Network Embeddings
Textual network embeddings aim to learn a low-dimensional representation for every node in the network so that both the structural and textual information from the networks can be well preserved in the representations.
BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization
The success of neural summarization models stems from the meticulous encodings of source articles.
Ranked #14 on
Text Summarization
on GigaWord
