Researches on dialogue empathy aim to endow an agent with the capacity of accurate understanding and proper responding for emotions. Existing models for empathetic dialogue generation focus on the emotion flow in one direction, that is, from the cont
ext to response. We argue that conducting an empathetic conversation is a bidirectional process, where empathy occurs when the emotions of two interlocutors could converge on the same point, i.e., reaching an emotion consensus. Besides, we also find that the empathetic dialogue corpus is extremely limited, which further restricts the model performance. To address the above issues, we propose a dual-generative model, Dual-Emp, to simultaneously construct the emotion consensus and utilize some external unpaired data. Specifically, our model integrates a forward dialogue model, a backward dialogue model, and a discrete latent variable representing the emotion consensus into a unified architecture. Then, to alleviate the constraint of paired data, we extract unpaired emotional data from open-domain conversations and employ Dual-Emp to produce pseudo paired empathetic samples, which is more efficient and low-cost than the human annotation. Automatic and human evaluations demonstrate that our method outperforms competitive baselines in producing coherent and empathetic responses.
Retrieval-based chatbot selects the appropriate response from candidates according to the context, which heavily depends on a response selection module. A response selection module is generally a scoring model to evaluate candidates and is usually tr
ained on the annotated positive response and sampled negative responses. Sampling negative responses lead to two risks: a). The sampled negative instances, especially that from random sampling methods, are mostly irrelevant to the dialogue context and too easy to be fitted at the training stage while causing a weak model in the real scenario. b). The so-called negative instances may be positive, which is known as the fake negative problem. To address the above issue, we employ pre-trained language models, such as the DialoGPT to construct more challenging negative instances to enhance the model robustness. Specifically, we provide garbled context to the pre-trained model to generate responses and filter the fake negative ones. In this way, our negative instances are fluent, context-related, and more challenging for the model to learn, while can not be positive. Extensive experiments show that our method brings significant and stable improvements on the dialogue response selection capacity.
Recent work has proposed several efficient approaches for generating gradient-based adversarial perturbations on embeddings and proved that the models performance and robustness can be improved when they are trained with these contaminated embeddings
. While they paid little attention to how to help the model to learn these adversarial samples more efficiently. In this work, we focus on enhancing the models ability to defend gradient-based adversarial attack during the models training process and propose two novel adversarial training approaches: (1) CARL narrows the original sample and its adversarial sample in the representation space while enlarging their distance from different labeled samples. (2) RAR forces the model to reconstruct the original sample from its adversarial representation. Experiments show that the proposed two approaches outperform strong baselines on various text classification datasets. Analysis experiments find that when using our approaches, the semantic representation of the input sentence wont be significantly affected by adversarial perturbations, and the models performance drops less under adversarial attack. That is to say, our approaches can effectively improve the robustness of the model. Besides, RAR can also be used to generate text-form adversarial samples.
Loading models pre-trained on the large-scale corpus in the general domain and fine-tuning them on specific downstream tasks is gradually becoming a paradigm in Natural Language Processing. Previous investigations prove that introducing a further pre
-training phase between pre-training and fine-tuning phases to adapt the model on the domain-specific unlabeled data can bring positive effects. However, most of these further pre-training works just keep running the conventional pre-training task, e.g., masked language model, which can be regarded as the domain adaptation to bridge the data distribution gap. After observing diverse downstream tasks, we suggest that different tasks may also need a further pre-training phase with appropriate training tasks to bridge the task formulation gap. To investigate this, we carry out a study for improving multiple task-oriented dialogue downstream tasks through designing various tasks at the further pre-training phase. The experiment shows that different downstream tasks prefer different further pre-training tasks, which have intrinsic correlation and most further pre-training tasks significantly improve certain target tasks rather than all. Our investigation indicates that it is of great importance and effectiveness to design appropriate further pre-training tasks modeling specific information that benefit downstream tasks. Besides, we present multiple constructive empirical conclusions for enhancing task-oriented dialogues.
Automatically composing pop music with a satisfactory structure is an attractive but challenging topic. Although the musical structure is easy to be perceived by human, it is difficult to be described clearly and defined accurately. And it is still f
ar from being solved that how we should model the structure in pop music generation. In this paper, we propose to leverage harmony-aware learning for structure-enhanced pop music generation. On the one hand, one of the participants of harmony, chord, represents the harmonic set of multiple notes, which is integrated closely with the spatial structure of music, texture. On the other hand, the other participant of harmony, chord progression, usually accompanies with the development of the music, which promotes the temporal structure of music, form. Besides, when chords evolve into chord progression, the texture and the form can be bridged by the harmony naturally, which contributes to the joint learning of the two structures. Furthermore, we propose the Harmony-Aware Hierarchical Music Transformer (HAT), which can exploit the structure adaptively from the music, and interact on the music tokens at multiple levels to enhance the signals of the structure in various musical elements. Results of subjective and objective evaluations demonstrate that HAT significantly improves the quality of generated music, especially in the structureness.
Human motion prediction, which plays a key role in computer vision, generally requires a past motion sequence as input. However, in real applications, a complete and correct past motion sequence can be too expensive to achieve. In this paper, we prop
ose a novel approach to predict future human motions from a much weaker condition, i.e., a single image, with mixture density networks (MDN) modeling. Contrary to most existing deep human motion prediction approaches, the multimodal nature of MDN enables the generation of diverse future motion hypotheses, which well compensates for the strong stochastic ambiguity aggregated by the single input and human motion uncertainty. In designing the loss function, we further introduce an energy-based prior over learnable parameters of MDN to maintain motion coherence, as well as improve the prediction accuracy. Our trained model directly takes an image as input and generates multiple plausible motions that satisfy the given condition. Extensive experiments on two standard benchmark datasets demonstrate the effectiveness of our method, in terms of prediction diversity and accuracy.
Human conversations consist of reasonable and natural topic flows, which are observed as the shifts of the mentioned concepts across utterances. Previous chatbots that incorporate the external commonsense knowledge graph prove that modeling the conce
pt shifts can effectively alleviate the dull and uninformative response dilemma. However, there still exists a gap between the concept relations in the natural conversation and those in the external commonsense knowledge graph, which is an issue to solve. Specifically, the concept relations in the external commonsense knowledge graph are not intuitively built from the conversational scenario but the world knowledge, which makes them insufficient for the chatbot construction. To bridge the above gap, we propose the method to supply more concept relations extracted from the conversational corpora and reconstruct an enhanced concept graph for the chatbot construction. In addition, we present a novel, powerful, and fast graph encoding architecture named the Edge-Transformer to replace the traditional GNN architecture. Experimental results on the Reddit conversation dataset indicate our proposed method significantly outperforms strong baseline systems and achieves new SOTA results. Further analysis individually proves the effectiveness of the enhanced concept graph and the Edge-Transformer architecture.
In this work, we study the problem of named entity recognition (NER) in a low resource scenario, focusing on few-shot and zero-shot settings. Built upon large-scale pre-trained language models, we propose a novel NER framework, namely SpanNER, which
learns from natural language supervision and enables the identification of never-seen entity classes without using in-domain labeled data. We perform extensive experiments on 5 benchmark datasets and evaluate the proposed method in the few-shot learning, domain transfer and zero-shot learning settings. The experimental results show that the proposed method can bring 10%, 23% and 26% improvements in average over the best baselines in few-shot learning, domain transfer and zero-shot learning settings respectively.
This paper considers a class of constrained convex stochastic composite optimization problems whose objective function is given by the summation of a differentiable convex component, together with a nonsmooth but convex component. The nonsmooth compo
nent has an explicit max structure that may not easy to compute its proximal mapping. In order to solve these problems, we propose a mini-batch stochastic Nesterovs smoothing (MSNS) method. Convergence and the optimal iteration complexity of the method are established. Numerical results are provided to illustrate the efficiency of the proposed MSNS method for a support vector machine (SVM) model.
Recently the splitting of elliptic flow $v_2$ at finite rapidities has been proposed as a result of the global vorticity in non-central relativistic heavy ion collisions. Using a multi-phase transport model that automatically includes the vorticity f
ield and flow fluctuations, we confirm the left-right (i.e., on opposite sides of the impact parameter axis) splitting of the elliptic flow at finite rapidities. However, we find that this $v_2$ splitting is a result of the non-zero directed flow $v_1$ at finite rapidities, with the splitting magnitude $approx 8v_1/3pi$. As a result, the $v_2$ splitting vanishes at zero transverse momentum ($p_{rm T}$), and its magnitude and sign may have non-trivial dependences on $p_{rm T}$, centrality, collision energy, and hadron species. Since the left-right $v_2$ splitting is a combined effect of $v_1$ and $v_2$, it will benefit studies of the three-dimensional structure and dynamics of the dense matter.
