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Found 17 papers, 7 papers with code
Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation
Here, we deal with these issues on two aspects: (1) We enhance the prior selection module with the necessary posterior information obtained from the specially designed Posterior Information Prediction Module (PIPM); (2) We propose a Knowledge Distillation Based Training Strategy (KDBTS) to train the decoder with the knowledge selected from the prior distribution, removing the exposure bias of knowledge selection.
Learning to Use Tools via Cooperative and Interactive Agents
Tool learning empowers large language models (LLMs) as agents to use external tools to extend their capability.
Continual Named Entity Recognition without Catastrophic Forgetting
This issue is intensified in CNER due to the consolidation of old entity types from previous steps into the non-entity type at each step, leading to what is known as the semantic shift problem of the non-entity type.
Continual Named Entity Recognition
named-entity-recognition
+1
Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognition
However, INER faces the challenge of catastrophic forgetting specific for incremental learning, further aggravated by background shift (i. e., old and future entity types are labeled as the non-entity type in the current task).
Matching-based Term Semantics Pre-training for Spoken Patient Query Understanding
Medical Slot Filling (MSF) task aims to convert medical queries into structured information, playing an essential role in diagnosis dialogue systems.
Crucial Semantic Classifier-based Adversarial Learning for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA), which aims to explore the transferrable features from a well-labeled source domain to a related unlabeled target domain, has been widely progressed.
Consecutive Knowledge Meta-Adaptation Learning for Unsupervised Medical Diagnosis
In the SAP, the semantic knowledge learned from the source lesion domain is transferred to consecutive target lesion domains.
HiVLP: Hierarchical Vision-Language Pre-Training for Fast Image-Text Retrieval
It also achieves about +4. 9 AR on COCO and +3. 8 AR on Flickr30K than LightingDot and achieves comparable performance with the state-of-the-art (SOTA) fusion-based model METER.
Improving Cross-Modal Understanding in Visual Dialog via Contrastive Learning
Visual Dialog is a challenging vision-language task since the visual dialog agent needs to answer a series of questions after reasoning over both the image content and dialog history.
TSAM: A Two-Stream Attention Model for Causal Emotion Entailment
Causal Emotion Entailment (CEE) aims to discover the potential causes behind an emotion in a conversational utterance.
Ranked #3 on
Causal Emotion Entailment
on RECCON
Multimodal Incremental Transformer with Visual Grounding for Visual Dialogue Generation
Visual dialogue is a challenging task since it needs to answer a series of coherent questions on the basis of understanding the visual environment.
GoG: Relation-aware Graph-over-Graph Network for Visual Dialog
Specifically, GoG consists of three sequential graphs: 1) H-Graph, which aims to capture coreference relations among dialog history; 2) History-aware Q-Graph, which aims to fully understand the question through capturing dependency relations between words based on coreference resolution on the dialog history; and 3) Question-aware I-Graph, which aims to capture the relations between objects in an image based on fully question representation.
Learning to Ground Visual Objects for Visual Dialog
Specifically, a posterior distribution over visual objects is inferred from both context (history and questions) and answers, and it ensures the appropriate grounding of visual objects during the training process.
Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer
For one thing, speakers often rely on the context and commonsense knowledge to express emotions; for another, most utterances contain neutral emotion in conversations, as a result, the confusion between a few non-neutral utterances and much more neutral ones restrains the emotion recognition performance.
A Working Memory Model for Task-oriented Dialog Response Generation
Our WMM2Seq adopts a working memory to interact with two separated long-term memories, which are the episodic memory for memorizing dialog history and the semantic memory for storing KB tuples.
