Building automatic technical support system is an important yet challenge task. Conceptually, to answer a user question on a technical forum, a human expert has to first retrieve relevant documents, and then read them carefully to identify the answer snippet. Despite huge success the researchers have achieved in coping with general domain question answering (QA), much less attentions have been paid for investigating technical QA. Specifically, existing methods suffer from several unique challenges (i) the question and answer rarely overlaps substantially and (ii) very limited data size. In this paper, we propose a novel framework of deep transfer learning to effectively address technical QA across tasks and domains. To this end, we present an adjustable joint learning approach for document retrieval and reading comprehension tasks. Our experiments on the TechQA demonstrates superior performance compared with state-of-the-art methods.

Question Answering (QA) on Electronic Health Records (EHR), namely EHR QA, can work as a crucial milestone towards developing an intelligent agent in healthcare. EHR data are typically stored in a relational database, which can also be converted to a Directed Acyclic Graph (DAG), allowing two approaches for EHR QA: Table-based QA and Knowledge Graph-based QA. We hypothesize that the graph-based approach is more suitable for EHR QA as graphs can represent relations between entities and values more naturally compared to tables, which essentially require JOIN operations. To validate our hypothesis, we first construct EHR QA datasets based on MIMIC-III, where the same question-answer pairs are represented in SQL (table-based) and SPARQL (graph-based), respectively. We then test a state-of-the-art EHR QA model on both datasets where the model demonstrated superior QA performance on the SPARQL version. Finally, we open-source both MIMICSQL* and MIMIC-SPARQL* to encourage further EHR QA research in both direction

Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evidence from different paragraphs. It is a more challenging yet under-explored task compared to conventional single-hop QG, where the questions are generated from the sentence containing the answer or nearby sentences in the same paragraph without complex reasoning. To address the additional challenges in multi-hop QG, we propose Multi-Hop Encoding Fusion Network for Question Generation (MulQG), which does context encoding in multiple hops with Graph Convolutional Network and encoding fusion via an Encoder Reasoning Gate. To the best of our knowledge, we are the first to tackle the challenge of multi-hop reasoning over paragraphs without any sentence-level information. Empirical results on HotpotQA dataset demonstrate the effectiveness of our method, in comparison with baselines on automatic evaluation metrics. Moreover, from the human evaluation, our proposed model is able to generate fluent questions with high completeness and outperforms the strongest baseline by 20.8% in the multi-hop evaluation. The code is publicly available at https://github.com/HLTCHKUST/MulQG}{https://github.com/HLTCHKUST/MulQG .

BERT and its variants have achieved state-of-the-art performance in various NLP tasks. Since then, various works have been proposed to analyze the linguistic information being captured in BERT. However, the current works do not provide an insight into how BERT is able to achieve near human-level performance on the task of Reading Comprehension based Question Answering. In this work, we attempt to interpret BERT for RCQA. Since BERT layers do not have predefined roles, we define a layer's role or functionality using Integrated Gradients. Based on the defined roles, we perform a preliminary analysis across all layers. We observed that the initial layers focus on query-passage interaction, whereas later layers focus more on contextual understanding and enhancing the answer prediction. Specifically for quantifier questions (how much/how many), we notice that BERT focuses on confusing words (i.e., on other numerical quantities in the passage) in the later layers, but still manages to predict the answer correctly. The fine-tuning and analysis scripts will be publicly available at https://github.com/iitmnlp/BERT-Analysis-RCQA .

In spoken question answering, QA systems are designed to answer questions from contiguous text spans within the related speech transcripts. However, the most natural way that human seek or test their knowledge is via human conversations. Therefore, we propose a new Spoken Conversational Question Answering task (SCQA), aiming at enabling QA systems to model complex dialogues flow given the speech utterances and text corpora. In this task, our main objective is to build a QA system to deal with conversational questions both in spoken and text forms, and to explore the plausibility of providing more cues in spoken documents with systems in information gathering. To this end, instead of adopting automatically generated speech transcripts with highly noisy data, we propose a novel unified data distillation approach, DDNet, which directly fuse audio-text features to reduce the misalignment between automatic speech recognition hypotheses and the reference transcriptions. In addition, to evaluate the capacity of QA systems in a dialogue-style interaction, we assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with more than 120k question-answer pairs. Experiments demonstrate that our proposed method achieves superior performance in spoken conversational question answering.

Snapchat's augmented reality dreams might be starting to look a bit more realistic. The company has been subtly improving its AR-powered Lenses every year, improving the technical odds-and-ends and strengthening its dev platform. The result is that today, more than 170 million people -- over three-quarters of Snap's daily active users -- access the app's augmented reality features on a daily basis, the company says. Two years ago, Snap shared that creators had designed over 100,000 lenses on the platform; now Snap says there have been more than 1 million lenses created. The goofy filters are bringing users to the app and the company is slowly building a more interconnected platform around augmented reality that is beginning to look more and more promising.

Noname manuscript No. (will be inserted by the editor) Abstract Video Question Answering (Video QA) challenges show consistent improvements over state-of-the-art methods modelers in multiple fronts. Modeling video necessitates on well-studied benchmarks including large-scale real-world building not only spatiotemporal models for the dynamic datasets such as TGIF-QA and TVQA, demonstrating the visual channel but also multimodal structures for associated strong capabilities of our CRN unit and the HCRN for complex information channels such as subtitles or audio. To the best of our knowledge, adds at least two more layers of complexity - selecting relevant the HCRN is the very first method attempting to handle content for each channel in the context of the linguistic long and short-form multimodal Video QA at the same time. To address these modules · Hierarchy requirements, we start with two insights: (a) content selection and relation construction can be jointly encapsulated into a conditional computational structure, and (b) video-length 1 Introduction structures can be composed hierarchically. For (a) this paper introduces a general-reusable reusable neural unit dubbed Answering natural questions about a video is a powerful Conditional Relation Network (CRN) taking as input a set of demonstration of cognitive capability. The task involves acquisition tensorial objects and translating into a new set of objects that and manipulation of spatiotemporal visual, acoustic encode relations of the inputs. The generic design of CRN and linguistic representations from the video guided by helps ease the common complex model building process the compositional semantics of linguistic cues [1, 2, 3, 4, 5, of Video QA by simple block stacking and rearrangements 6]. As questions are potentially unconstrained, Video QA with flexibility in accommodating diverse input modalities requires deep modeling capacity to encode and represent crucial and conditioning features across both visual and linguistic multimodal video properties such as linguistic content, domains. As a result, we realize insight (b) by introducing object permanence, motion profiles, prolonged actions, and Hierarchical Conditional Relation Networks (HCRN) for varying-length temporal relations in a hierarchical manner. The HCRN primarily aims at exploiting intrinsic For Video QA, the visual and textual representations should properties of the visual content of a video as well as its accompanying ideally be question-specific and answer-ready.

Document interpretation and dialog understanding are the two major challenges for conversational machine reading. In this work, we propose Discern, a discourse-aware entailment reasoning network to strengthen the connection and enhance the understanding for both document and dialog. Specifically, we split the document into clause-like elementary discourse units (EDU) using a pre-trained discourse segmentation model, and we train our model in a weakly-supervised manner to predict whether each EDU is entailed by the user feedback in a conversation. Based on the learned EDU and entailment representations, we either reply to the user our final decision "yes/no/irrelevant" of the initial question, or generate a follow-up question to inquiry more information. Our experiments on the ShARC benchmark (blind, held-out test set) show that Discern achieves state-of-the-art results of 78.3% macro-averaged accuracy on decision making and 64.0 BLEU1 on follow-up question generation. Code and models are released at https://github.com/Yifan-Gao/Discern.

IBM Watson is considered the grandfather of cognitive search and natural language processing, and Scott Parker was there for its birth. The company he worked for at the time, Vivisimo, had been acquired by IBM, and its enterprise search technology formed a major component of the Watson solution. That was Parker's introduction to the art and science of enterprise search technologies. Now the director of product marketing at enterprise search technology company Sinequa, Parker leverages the power of intelligent search to help extract valuable insights from customer's data. Sinequa is a sponsor of Simpler Media Group's Digital Workplace Experience, starting today as a free, virtual event.

What is Customer Effort, and how can it be measured from chatbot conversations? And, how can Disambiguation improve Customer Effort? Aslo, can Automatic Learning be employed to improve Customer Effort over time? Below you will find an explanation of what customer effort is. And a complete how to guide on extracting Customer Effort from your IBM Watson Assistant chatbot. Customer effort is an extremely convenient metric to measure your chatbots performance.