A Controllable Model of Grounded Response Generation

Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses. Attempts to boost informativeness alone come at the expense of factual accuracy, as attested by pretrained language models' propensity to "hallucinate" facts. While this may be mitigated by access to background knowledge, there is scant guarantee of relevance and informativeness in generated responses. We propose a framework that we call controllable grounded response generation (CGRG), in which lexical control phrases are either provided by a user or automatically extracted by a control phrase predictor from dialogue context and grounding knowledge. Quantitative and qualitative results show that, using this framework, a transformer based model with a novel inductive attention mechanism, trained on a conversation-like Reddit dataset, outperforms strong generation baselines.Comment: AAAI 202

Organizing societal space within globalization: Bringing society back in

The notion of 'society' is increasingly debated, recently, under the impact of 'globalization'. This debate is carried out in both sociology and business studies, and it also has implications in political theory. A theoretical grounding of society is provided following G.H. Mead, which bears sufficient regard to actors and avoids determinism. Society is conceptualized as 'societal space', open to layering in different forms. Incongruent layering is then put forward as a feature of societal evolution which has hitherto been neglected as an engine of modernization. This form of layering is also suggested to be important for current debates. Following this concept, the business and organizational literature can be linked with social theory in a way which shows how 'provincialization' of identity, institutions and culture is pervasively linked with the extension of horizons of action under globalization. Various comparative findings are adduced to show how the dialectics of globalization and provincialization work, and how socio-institutional patterns interact with the evolution of enterprise strategies in order to fuel this dialectic. In such an evolution, society has an important part to play. But this is not because society re-asserts itself as a co-extensive entity on a higher plane. Instead, it is precisely the layering of societal space which makes societal effects a necessary concept. --

Wasserstein Autoencoders with Mixture of Gaussian Priors for Stylized Text Generation

Probabilistic text generation is an important application of Natural Language Processing (NLP). Variational autoencoders and Wasserstein autoencoders are two widely used methods for text generation. New research efforts focus on improving the quality of the generated samples for these two methods. While Wasserstein autoencoders are effective for text generation, they are unable to control the topic of generated text, even when the training dataset has samples from multiple categories with different styles. We present a semi-supervised approach using Wasserstein autoencoders and a mixture of Gaussian priors for topic-aware sentence generation. Our model is trained on a multi-class dataset and generates sentences in the style/topic of a desired class. It is also capable of interpolating multiple classes. Moreover, we can train our model on relatively small datasets. While a regular WAE or VAE cannot generate diverse sentences with few training samples, our approach generates diverse sentences and preserves the style and the content of the desired classes