Moreover, to verify the generality of the model, we also conduct experiments on two common sentiment analysis datasets.
In an open-domain dialogue system, the consistent persona is a key factor to generate real and coherent dialogues.
Consequently, our work complements research on the performance of MLLMs in multimodal comprehension tasks, achieving a more comprehensive and holistic evaluation of MLLMs.
However, the application of CEs has been hindered by two main challenges, namely general user preferences and variable ML systems.
There are risks in using eference-free evaluators based on LLMs to evaluate the quality of dialogue responses.
We investigate response generation for multi-turn dialogue in generative-based chatbots.
To tackle this problem, we propose a novel method called Multimodal Probabilistic Fusion Prompts (MultiPoint) that leverages diverse cues from different modalities for multimodal sentiment detection in the few-shot scenario.
Sparsity of formal knowledge and roughness of non-ontological construction make sparsity problem particularly prominent in Open Knowledge Graphs (OpenKGs).
Current end-to-end retrieval-based dialogue systems are mainly based on Recurrent Neural Networks or Transformers with attention mechanisms.
Building dialogue generation systems in a zero-shot scenario remains a huge challenge, since the typical zero-shot approaches in dialogue generation rely heavily on large-scale pre-trained language generation models such as GPT-3 and T5.
We investigate response selection for multi-turn conversation in retrieval-based chatbots.
We observe that the answer has strong semantic coherence to its question and post, which can be used to guide question generation.
Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset.
Then the attention-based CNN model is incorporated into a novel adversarial cross-lingual learning framework, in which with the help of user properties as bridge between languages, we can extract the language-specific features and language-independent features to enrich the user post representation so as to alleviate the data insufficiency problem.