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Found 184 papers, 73 papers with code
Human-Human Health Coaching via Text Messages: Corpus, Annotation, and Analysis
In this paper, we discuss these schemas and briefly talk about their application for automatically extracting activity goals and annotating the second round of data, collected with different health coaches and patients.
Summarizing Behavioral Change Goals from SMS Exchanges to Support Health Coaches
In this paper, we present our work towards assisting health coaches by extracting the physical activity goal the user and coach negotiate via text messages.
Semantic Novelty Detection in Natural Language Descriptions
This paper proposes to study a fine-grained semantic novelty detection task, which can be illustrated with the following example.
Continual Learning Using Only Large Language Model Prompting
We introduce CLOB, a novel continual learning (CL) paradigm wherein a large language model (LLM) is regarded as a black box.
In-context Continual Learning Assisted by an External Continual Learner
However, incrementally learning each new task in ICL necessitates adding training examples from each class of the task to the prompt, which hampers scalability as the prompt length increases.
Continual Learning Using a Kernel-Based Method Over Foundation Models
This paper proposes a novel CIL method, called Kernel Linear Discriminant Analysis (KLDA), that can effectively avoid CF and ICS problems.
DESIRE: Dynamic Knowledge Consolidation for Rehearsal-Free Continual Learning
On the one hand, we retain only two sets of LoRA parameters for merging and propose dynamic representation consolidation to calibrate the merged feature representation.
Privacy-Preserving Resilient Vector Consensus
This paper studies privacy-preserving resilient vector consensus in multi-agent systems against faulty agents, where normal agents can achieve consensus within the convex hull of their initial states while protecting state vectors from being disclosed.
Boosting LLM Translation Skills without General Ability Loss via Rationale Distillation
These abilities, which are developed using proprietary and unavailable training data, make existing continual instruction tuning methods ineffective.
Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach
Graph CIL (GCIL) follows the same setting but needs to deal with graph tasks (e. g., node classification in a graph).
Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding Sample
Specifically, we explore the mechanism of self-attention weight distribution, in which the self-attention weight distribution of each AU is regarded as spatial distribution and is adaptively learned under the constraint of location-predefined attention and the guidance of AU detection.
Layer Swapping for Zero-Shot Cross-Lingual Transfer in Large Language Models
We focus on mathematical reasoning and without in-language math data, facilitate cross-lingual transfer by composing language and math capabilities.
Predicting Lung Cancer Patient Prognosis with Large Language Models
The experimental results demonstrate that LLMs can achieve competitive, and in some tasks superior, performance in lung cancer prognosis prediction compared to data-driven logistic regression models despite not using additional patient data.
The Llama 3 Herd of Models
This paper presents a new set of foundation models, called Llama 3.
Ranked #3 on
Multi-task Language Understanding
on MMLU
(using extra training data)
The Power of Combining Data and Knowledge: GPT-4o is an Effective Interpreter of Machine Learning Models in Predicting Lymph Node Metastasis of Lung Cancer
We then designed a prompt template to integrate the patient data with the predicted probability from the machine learning model.
Talk With Human-like Agents: Empathetic Dialogue Through Perceptible Acoustic Reception and Reaction
This oversight can lead to misinterpretations of speakers' intentions, resulting in inconsistent or even contradictory responses within dialogues.
Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective
Foundation Models (FMs) have demonstrated remarkable insights into the relational dynamics of the world, leading to the crucial question: how do these models acquire an understanding of world hybrid relations?
The current status of large language models in summarizing radiology report impressions
Besides the automatic quantitative evaluation metrics, we define five human evaluation metrics, i. e., completeness, correctness, conciseness, verisimilitude, and replaceability, to evaluate the semantics of the generated impressions.
Probing Language Models for Pre-training Data Detection
Large Language Models (LLMs) have shown their impressive capabilities, while also raising concerns about the data contamination problems due to privacy issues and leakage of benchmark datasets in the pre-training phase.
Safety through Permissibility: Shield Construction for Fast and Safe Reinforcement Learning
Designing Reinforcement Learning (RL) solutions for real-life problems remains a significant challenge.
Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal Summarization and Text-Units-Text Generation
Health coaching helps patients achieve personalized and lifestyle-related goals, effectively managing chronic conditions and alleviating mental health issues.
Towards Enhancing Health Coaching Dialogue in Low-Resource Settings
In this paper, we propose to build a dialogue system that converses with the patients, helps them create and accomplish specific goals, and can address their emotions with empathy.
A Theory for Length Generalization in Learning to Reason
Length generalization (LG) is a challenging problem in learning to reason.
Retrieval Augmented Cross-Modal Tag Recommendation in Software Q&A Sites
For the retrieval-augmented representations, we employ a cross-modal context-aware attention to leverage the main modality description for targeted feature extraction across the submodalities title and code.
How to Forget Clients in Federated Online Learning to Rank?
In a FOLTR system, a ranker is learned by aggregating local updates to the global ranking model.
SciConNav: Knowledge navigation through contextual learning of extensive scientific research trajectories
New knowledge builds upon existing foundations, which means an interdependent relationship exists between knowledge, manifested in the historical development of the scientific system for hundreds of years.
Grounding for Artificial Intelligence
A core function of intelligence is grounding, which is the process of connecting the natural language and abstract knowledge to the internal representation of the real world in an intelligent being, e. g., a human.
Conditions for Length Generalization in Learning Reasoning Skills
However, numerous evaluations of the reasoning capabilities of LLMs have also showed some limitations.
Continual Learning: Applications and the Road Forward
Continual learning is a subfield of machine learning, which aims to allow machine learning models to continuously learn on new data, by accumulating knowledge without forgetting what was learned in the past.
Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking
In the field of information retrieval, Query Likelihood Models (QLMs) rank documents based on the probability of generating the query given the content of a document.
Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks
To our knowledge, only one method has been proposed to learn a sequence of mixed tasks.
Sequential Tag Recommendation
With the development of Internet technology and the expansion of social networks, online platforms have become an important way for people to obtain information.
Class Incremental Learning via Likelihood Ratio Based Task Prediction
An emerging theory-guided approach (called TIL+OOD) is to train a task-specific model for each task in a shared network for all tasks based on a task-incremental learning (TIL) method to deal with catastrophic forgetting.
Zero-shot information extraction from radiological reports using ChatGPT
Information extraction is the strategy to transform the sequence of characters into structured data, which can be employed for secondary analysis.
CT-Net: Arbitrary-Shaped Text Detection via Contour Transformer
Contour based scene text detection methods have rapidly developed recently, but still suffer from inaccurate frontend contour initialization, multi-stage error accumulation, or deficient local information aggregation.
Parameter-Level Soft-Masking for Continual Learning
Although several techniques have achieved learning with no CF, they attain it by letting each task monopolize a sub-network in a shared network, which seriously limits knowledge transfer (KT) and causes over-consumption of the network capacity, i. e., as more tasks are learned, the performance deteriorates.
Class-Incremental Learning based on Label Generation
Despite the great success of pre-trained language models, it is still a challenge to use these models for continual learning, especially for the class-incremental learning (CIL) setting due to catastrophic forgetting (CF).
Dealing with Cross-Task Class Discrimination in Online Continual Learning
A novel optimization objective with a gradient-based adaptive method is proposed to dynamically deal with the problem in the online CL process.
Sentiment Analysis in the Era of Large Language Models: A Reality Check
This paper aims to provide a comprehensive investigation into the capabilities of LLMs in performing various sentiment analysis tasks, from conventional sentiment classification to aspect-based sentiment analysis and multifaceted analysis of subjective texts.
Do We Need an Encoder-Decoder to Model Dynamical Systems on Networks?
As deep learning gains popularity in modelling dynamical systems, we expose an underappreciated misunderstanding relevant to modelling dynamics on networks.
Analyzing and Reducing the Performance Gap in Cross-Lingual Transfer with Fine-tuning Slow and Fast
Existing research has shown that a multilingual pre-trained language model fine-tuned with one (source) language also performs well on downstream tasks for non-source languages, even though no fine-tuning is done on these languages.
A Unified Evaluation Framework for Novelty Detection and Accommodation in NLP with an Instantiation in Authorship Attribution
To initiate a systematic research in this important area of 'dealing with novelties', we introduce 'NoveltyTask', a multi-stage task to evaluate a system's performance on pipelined novelty 'detection' and 'accommodation' tasks.
Open-World Continual Learning: Unifying Novelty Detection and Continual Learning
The paper then proves that the theory can be generalized or extended to open-world CIL, which is the proposed open-world continual learning, that can perform CIL in the open world and detect future or open-world OOD data.
Grab What You Need: Rethinking Complex Table Structure Recognition with Flexible Components Deliberation
Recently, Table Structure Recognition (TSR) task, aiming at identifying table structure into machine readable formats, has received increasing interest in the community.
Continual Pre-training of Language Models
A novel proxy is also proposed to preserve the general knowledge in the original LM.
Ranked #1 on
Continual Pretraining
on ACL-ARC
Adapting a Language Model While Preserving its General Knowledge
This paper shows that the existing methods are suboptimal and proposes a novel method to perform a more informed adaptation of the knowledge in the LM by (1) soft-masking the attention heads based on their importance to best preserve the general knowledge in the LM and (2) contrasting the representations of the general and the full (both general and domain knowledge) to learn an integrated representation with both general and domain-specific knowledge.
Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images
Semantic Change Detection (SCD) refers to the task of simultaneously extracting the changed areas and the semantic categories (before and after the changes) in Remote Sensing Images (RSIs).
Ranked #2 on
Change Detection
on SECOND
Guiding Neural Entity Alignment with Compatibility
Making compatible predictions thus should be one of the goals of training an EA model along with fitting the labelled data: this aspect however is neglected in current methods.
Dependency-aware Self-training for Entity Alignment
Entity Alignment (EA), which aims to detect entity mappings (i. e. equivalent entity pairs) in different Knowledge Graphs (KGs), is critical for KG fusion.
Continual Learning of Natural Language Processing Tasks: A Survey
Continual learning (CL) is a learning paradigm that emulates the human capability of learning and accumulating knowledge continually without forgetting the previously learned knowledge and also transferring the learned knowledge to help learn new tasks better.
Lifelong and Continual Learning Dialogue Systems
This book introduces the new paradigm of lifelong learning dialogue systems to endow chatbots the ability to learn continually by themselves through their own self-initiated interactions with their users and working environments to improve themselves.
A Theoretical Study on Solving Continual Learning
Continual learning (CL) learns a sequence of tasks incrementally.
Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities
Much of the existing work on text novelty detection has been studied at the topic level, i. e., identifying whether the topic of a document or a sentence is novel or not.
Knowledge-Guided Exploration in Deep Reinforcement Learning
This paper proposes a new method to drastically speed up deep reinforcement learning (deep RL) training for problems that have the property of state-action permissibility (SAP).
Continual Training of Language Models for Few-Shot Learning
Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications.
Ranked #1 on
Continual Pretraining
on AG News
High-quality Task Division for Large-scale Entity Alignment
To include in the EA subtasks a high proportion of the potential mappings originally present in the large EA task, we devise a counterpart discovery method that exploits the locality principle of the EA task and the power of trained EA models.
A Multi-Head Model for Continual Learning via Out-of-Distribution Replay
Instead of using the saved samples in memory to update the network for previous tasks/classes in the existing approach, MORE leverages the saved samples to build a task specific classifier (adding a new classification head) without updating the network learned for previous tasks/classes.
Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddings
Learning task-oriented dialog policies via reinforcement learning typically requires large amounts of interaction with users, which in practice renders such methods unusable for real-world applications.
TextDCT: Arbitrary-Shaped Text Detection via Discrete Cosine Transform Mask
Arbitrary-shaped scene text detection is a challenging task due to the variety of text changes in font, size, color, and orientation.
Beyond Opinion Mining: Summarizing Opinions of Customer Reviews
In this tutorial, we present various aspects of opinion summarization that are useful for researchers and practitioners.
KETOD: Knowledge-Enriched Task-Oriented Dialogue
We also propose two new models, SimpleToDPlus and Combiner, for the proposed task.
Unsupervised Meta Learning With Multiview Constraints for Hyperspectral Image Small Sample set Classification
However, the existing methods based on meta learning still need to construct a labeled source data set with several pre-collected HSIs, and must utilize a large number of labeled samples for meta-training, which is actually time-consuming and labor-intensive.
Open-set Recognition via Augmentation-based Similarity Learning
In many practical scenarios, this is not the case because there are unknowns or unseen class samples in the test data, which is called the open set scenario, and the unknowns need to be detected.
Continual Learning Based on OOD Detection and Task Masking
Existing continual learning techniques focus on either task incremental learning (TIL) or class incremental learning (CIL) problem, but not both.
AI Autonomy : Self-Initiated Open-World Continual Learning and Adaptation
As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can (1) learn by themselves continually in a self-motivated and self-initiated manner rather than being retrained offline periodically on the initiation of human engineers and (2) accommodate or adapt to unexpected or novel circumstances.
Ensemble Semi-supervised Entity Alignment via Cycle-teaching
We also design a conflict resolution mechanism to resolve the alignment conflict when combining the new alignment of an aligner and that from its teacher.
Zero-Shot Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) typically requires in-domain annotated data for supervised training/fine-tuning.
Show, Deconfound and Tell: Image Captioning With Causal Inference
In the encoding stage, the IOD is able to disentangle the region-based visual features by deconfounding the visual confounder.
Continual Learning with Knowledge Transfer for Sentiment Classification
In this setting, the CL system learns a sequence of SC tasks incrementally in a neural network, where each task builds a classifier to classify the sentiment of reviews of a particular product category or domain.
Ranked #4 on
Continual Learning
on DSC (10 tasks)
Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks
To the best of our knowledge, no technique has been proposed to learn a sequence of mixed similar and dissimilar tasks that can deal with forgetting and also transfer knowledge forward and backward.
Ranked #1 on
Continual Learning
on F-CelebA (10 tasks)
Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks
This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks.
Ranked #3 on
Continual Learning
on ASC (19 tasks)
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning
Although several papers have tried to deal with both CF and KT, our experiments show that they suffer from serious CF when the tasks do not have much shared knowledge.
Ranked #1 on
Continual Learning
on DSC (10 tasks)
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks
The key novelty is a contrastive continual learning method that enables both knowledge transfer across tasks and knowledge distillation from old tasks to the new task, which eliminates the need for task ids in testing.
BNS: Building Network Structures Dynamically for Continual Learning
Continual learning (CL) of a sequence of tasks is often accompanied with the catastrophic forgetting(CF) problem.
Neural Collaborative Graph Machines for Table Structure Recognition
We also show that the proposed NCGM can modulate collaborative pattern of different modalities conditioned on the context of intra-modality cues, which is vital for diversified table cases.
Ranked #7 on
Table Recognition
on PubTabNet
Defeating Catastrophic Forgetting via Enhanced Orthogonal Weights Modification
what is the upper bound of the learningable tasks sequentially for a given CL method?
Self-Initiated Open World Learning for Autonomous AI Agents
As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can learn by themselves in a self-motivated and self-supervised manner rather than being retrained periodically on the initiation of human engineers using expanded training data.
ActiveEA: Active Learning for Neural Entity Alignment
Entity Alignment (EA) aims to match equivalent entities across different Knowledge Graphs (KGs) and is an essential step of KG fusion.
Partially Relaxed Masks for Lightweight Knowledge Transfer without Forgetting in Continual Learning
The goal of this work is to endow such systems with the additional ability to transfer knowledge among tasks when the tasks are similar and have shared knowledge to achieve higher accuracy.
Gradient Imbalance and solution in Online Continual learning
Most existing techniques for online continual learning are based on experience-replay.
Efficient Out-of-Distribution Detection via CVAE data Generation
Recently, contrastive loss with data augmentation and pseudo class creation has been shown to produce markedly better results for out-of-distribution (OOD) detection than previous methods.
Continual Learning Using Pseudo-Replay via Latent Space Sampling
PLS is not only simple and efficient but also does not invade data privacy due to the fact that it works in the latent feature space.
Zero-Shot Dialogue State Tracking via Cross-Task Transfer
Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data.
Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIP
In an out-of-distribution (OOD) detection problem, samples of known classes(also called in-distribution classes) are used to train a special classifier.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+2
Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification
In this paper, we propose a novel concept-based label embedding method that can explicitly represent the concept and model the sharing mechanism among classes for the hierarchical text classification.
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTracking
Zero-shot cross-domain dialogue state tracking (DST) enables us to handle unseen domains without the expense of collecting in-domain data.
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking
Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented dialogue in unseen domains without the expense of collecting in-domain data.
Analyzing the Forgetting Problem in Pretrain-Finetuning of Open-domain Dialogue Response Models
We find that mix-review effectively regularizes the finetuning process, and the forgetting problem is alleviated to some extent.
Learning to Dynamically Select Between Reward Shaping Signals
Automatic reward shaping is one approach to solving this problem, using automatic identification and modulation of shaping reward signals that are more informative about how agents should behave in any given scenario to learn and adapt faster.
Continual Learning in Task-Oriented Dialogue Systems
Continual learning in task-oriented dialogue systems can allow us to add new domains and functionalities through time without incurring the high cost of a whole system retraining.
A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data
Ramp metering that uses traffic signals to regulate vehicle flows from the on-ramps has been widely implemented to improve vehicle mobility of the freeway.
HRN: A Holistic Approach to One Class Learning
Existing neural network based one-class learning methods mainly use various forms of auto-encoders or GAN style adversarial training to learn a latent representation of the given one class of data.
Bayes-enhanced Lifelong Attention Networks for Sentiment Classification
After each sentiment classification task is learned, its knowledge is retained to help future task learning.
Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling
Understanding neural models is a major topic of interest in the deep learning community.
Attention Aware Cost Volume Pyramid Based Multi-view Stereo Network for 3D Reconstruction
This strategy estimates the depth map at coarsest level, while the depth maps at finer levels are considered as the upsampled depth map from previous level with pixel-wise depth residual.
Lifelong Knowledge Learning in Rule-based Dialogue Systems
One of the main weaknesses of current chatbots or dialogue systems is that they do not learn online during conversations after they are deployed.
MP-ResNet: Multi-path Residual Network for the Semantic segmentation of High-Resolution PolSAR Images
This CNN architecture can be used as a baseline method for future studies on the semantic segmentation of PolSAR images.
Using the Past Knowledge to Improve Sentiment Classification
It proposes a new lifelong learning model (called L2PG) that can retain and selectively transfer the knowledge learned in the past to help learn the new task.
Understanding Pre-trained BERT for Aspect-based Sentiment Analysis
Most features in the representation of an aspect are dedicated to the fine-grained semantics of the domain (or product category) and the aspect itself, instead of carrying summarized opinions from its context.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions
As there is no clean mapping for a user's free form utterance to an ontology, we first model the user preferences as estimated distributions over the system ontology and map the users' utterances to such distributions.
Adding Chit-Chat to Enhance Task-Oriented Dialogues
Existing dialogue corpora and models are typically designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e. g., booking hotels), open-domain chatbots aim at making socially engaging conversations.
A Knowledge-Driven Approach to Classifying Object and Attribute Coreferences in Opinion Mining
Classifying and resolving coreferences of objects (e. g., product names) and attributes (e. g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance.
Text Classification with Novelty Detection
In this paper, we propose a significantly more effective approach that converts the original problem to a pair-wise matching problem and then outputs how probable two instances belong to the same class.
Lifelong Learning Dialogue Systems: Chatbots that Self-Learn On the Job
Due to the huge amount of manual effort involved, they are difficult to scale and also tend to produce many errors ought to their limited ability to understand natural language and the limited knowledge in their KBs.
Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification
First, a deep densely connected convolutional network is considered for hyperspectral image classification.
Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification
This paper studies the task of comparative preference classification (CPC).
Feature Projection for Improved Text Classification
In this paper, we propose a novel angle to further improve this representation learning, i. e., feature projection.
User Memory Reasoning for Conversational Recommendation
We study a conversational recommendation model which dynamically manages users' past (offline) preferences and current (online) requests through a structured and cumulative user memory knowledge graph, to allow for natural interactions and accurate recommendations.
Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon
We refer to this problem as domain polarity-changes of words.
DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis
This paper focuses on learning domain-oriented language models driven by end tasks, which aims to combine the worlds of both general-purpose language models (such as ELMo and BERT) and domain-specific language understanding.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Computational Performance of a Germline Variant Calling Pipeline for Next Generation Sequencing
With the booming of next generation sequencing technology and its implementation in clinical practice and life science research, the need for faster and more efficient data analysis methods becomes pressing in the field of sequencing.
Transformation of Dense and Sparse Text Representations
The key idea of the proposed approach is to use a Forward Transformation to transform dense representations to sparse representations.
A Failure of Aspect Sentiment Classifiers and an Adaptive Re-weighting Solution
Aspect-based sentiment classification (ASC) is an important task in fine-grained sentiment analysis.~Deep supervised ASC approaches typically model this task as a pair-wise classification task that takes an aspect and a sentence containing the aspect and outputs the polarity of the aspect in that sentence.
Building an Application Independent Natural Language Interface
Traditional approaches to building natural language (NL) interfaces typically use a semantic parser to parse the user command and convert it to a logical form, which is then translated to an executable action in an application.
Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response Models
We find that mix-review effectively regularizes the finetuning process, and the forgetting problem is alleviated to some extent.
Learning from Positive and Unlabeled Data with Adversarial Training
Positive-unlabeled (PU) learning learns a binary classifier using only positive and unlabeled examples without labeled negative examples.
Continual Learning via Principal Components Projection
The idea is that in learning a new task, if we can ensure that the gradient updates will only occur in the orthogonal directions to the input vectors of the previous tasks, then the weight updates for learning the new task will not affect the previous tasks.
Learning with Noisy Labels for Sentence-level Sentiment Classification
We propose a novel DNN model called NetAb (as shorthand for convolutional neural Networks with Ab-networks) to handle noisy labels during training.
Modeling Multi-Action Policy for Task-Oriented Dialogues
Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system.
Flexibly-Structured Model for Task-Oriented Dialogues
It is based on a simple and practical yet very effective sequence-to-sequence approach, where language understanding and state tracking tasks are modeled jointly with a structured copy-augmented sequential decoder and a multi-label decoder for each slot.
Lifelong and Interactive Learning of Factual Knowledge in Dialogues
Dialogue systems are increasingly using knowledge bases (KBs) storing real-world facts to help generate quality responses.
Forward and Backward Knowledge Transfer for Sentiment Classification
That is, it is possible to improve the NB classifier for a task by improving its model parameters directly by using the retained knowledge from other tasks.
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction
This paper focuses on two related subtasks of aspect-based sentiment analysis, namely aspect term extraction and aspect sentiment classification, which we call aspect term-polarity co-extraction.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
GSN: A Graph-Structured Network for Multi-Party Dialogues
Existing neural models for dialogue response generation assume that utterances are sequentially organized.
Spectral Perturbation Meets Incomplete Multi-view Data
In this work, we show a strong link between perturbation risk bounds and incomplete multi-view clustering.
Controlled CNN-based Sequence Labeling for Aspect Extraction
The modified CNN has two types of control modules.
A Guider Network for Multi-Dual Learning
A large amount of parallel data is needed to train a strong neural machine translation (NMT) system.
Overcoming Catastrophic Forgetting via Model Adaptation
Several continual learning methods have been proposed to address the problem.