Existing Question Answering (QA) systems are limited in their ability to answer questions from unseen domains or any out-of-domain distributions, making them less reliable for deployment in real scenarios. Importantly, all existing QA domain adaptation methods are either based on generating synthetic data or pseudo-labeling the target domain data. Domain adaptation methods relying on synthetic data and pseudo-labeling suffer from either the need for extensive computational resources or an additional overhead of carefully selecting the confidence threshold to distinguish noisy examples from the training dataset. In this paper, we propose unsupervised domain adaptation for an unlabeled target domain by transferring the target representation close to the source domain without using supervision from the target domain. To achieve this, we introduce the idea of domain-invariant fine-tuning along with adversarial label correction (DomainInv) to identify target instances that are distant from the source domain. This involves learning the domain invariant feature encoder to minimize the distance between such target instances and source instances class-wisely. This eliminates the possibility of learning features of the target domain that are still close to the source support but are ambiguous. The evaluation of our QA domain adaptation method, namely DomainInv, on multiple target QA datasets reveals a performance improvement over the strongest baseline.

E-commerce websites (e.g. Amazon, Alibaba) have a plethora of structured and unstructured information (text and images) present on the product pages. Sellers often don’t label or mislabel values of the attributes (e.g. color, size etc.) for their products. Automatically identifying these attribute values from an eCommerce product page that contains both text and images is a challenging task, especially when the attribute value is not explicitly mentioned in the catalog. In this paper, we present a scalable solution for this problem where we pose attribute extraction problem as a question-answering task, which we solve using MXT, that consists of three key components: (i) MAG (Multimodal Adaptation Gate), (ii) Xception network, and (iii) T5 encoder-decoder. Our system consists of a generative model that generates attribute-values for a given product by using both textual and visual characteristics (e.g. images) of the product. We show that our system is capable of handling zero-shot attribute prediction (when attribute value is not seen in training data) and value-absent prediction (when attribute value is not mentioned in the text) which are missing in traditional classification-based and NER-based models respectively. We have trained our models using distant supervision, removing dependency on human labeling, thus making them practical for real-world applications. With this framework, we are able to train a single model for 1000s of (product-type, attribute) pairs, thus reducing the overhead of training and maintaining separate models. Extensive experiments on two real world datasets (total 57 attributes) show that our framework improves the absolute recall@90P by 10.16% and 6.9 from the existing state of the art models. In a popular e-commerce store, we have productionized our models that cater to 12K (product-type, attribute) pairs, and have extracted 150MM attribute values.

An intelligent dialogue system in a multi-turn setting should not only generate the responses which are of good quality, but it should also generate the responses which can lead to long-term success of the dialogue. Although, the current approaches improved the response quality, but they over-look the training signals present in the dialogue data. We can leverage these signals to generate the weakly supervised training data for learning dialog policy and reward estimator, and make the policy take actions (generates responses) which can foresee the future direction for a successful (rewarding) conversation. We simulate the dialogue between an agent and a user (modelled similar to an agent with supervised learning objective) to interact with each other. The agent uses dynamic blocking to generate ranked diverse responses and exploration-exploitation to select among the Top-K responses. Each simulated state-action pair is evaluated (works as a weak annotation) with three quality modules: Semantic Relevant, Semantic Coherence and Consistent Flow. Empirical studies with two benchmarks indicate that our model can significantly out-perform the response quality and lead to a successful conversation on both automatic evaluation and human judgment.