"Questions are asked and answered every day. Question answering (QA) technology aims to deliver the same facility online. It goes further than the more familiar search based on keywords (as in Google, Yahoo, and other search engines), in attempting to recognize what a question expresses and to respond with an actual answer. This simplifies things for users in two ways. First, questions do not often translate into a simple list of keywords. ...Second, QA takes responsibility for providing answers, rather than a searchable list of links to potentially relevant documents (web pages), highlighted by snippets of text that show how the query matched the documents."
– from Bonnie Webber & Nick Webb. Question Answering. In The Handbook of Computational Linguistics and Natural Language Processing. Alexander Clark, Chris Fox, Shalom Lappin (Eds.). Wiley, 2010.
Boosting its NLP capabilities, IBM has launched new innovative capabilities in IBM Watson Discovery and IBM Watson Assistant, which will empower businesses to deploy and scale sophisticated AI systems. It will leverage NLP with accuracy and efficiency, all while requiring fewer data and training time. It is another significant step by the tech giant to offer advanced ability to understand the language of business. With an aim to bring better NLP and NLU offerings to users in its enterprise products, the company has yet again shown its drive to take NLP efforts to a newer height. While recent announcements by IBM focus around language, explainability, and workplace automation, the update around its language capabilities include reading comprehension, FAQ extraction and improving interactions in Watson Assistant.
Textbook Question Answering (TQA) is a task that one should answer a diagram/non-diagram question given a large multi-modal context consisting of abundant essays and diagrams. We argue that the explainability of this task should place students as a key aspect to be considered. To address this issue, we devise a novel architecture towards span-level eXplanations of the TQA (XTQA) based on our proposed coarse-to-fine grained algorithm, which can provide not only the answers but also the span-level evidences to choose them for students. This algorithm first coarsely chooses top $M$ paragraphs relevant to questions using the TF-IDF method, and then chooses top $K$ evidence spans finely from all candidate spans within these paragraphs by computing the information gain of each span to questions. Experimental results shows that XTQA significantly improves the state-of-the-art performance compared with baselines. The source code is available at https://github.com/keep-smile-001/opentqa
Existing studies on question answering on knowledge bases (KBQA) mainly operate with the standard i.i.d assumption, i.e., training distribution over questions is the same as the test distribution. However, i.i.d may be neither reasonably achievable nor desirable on large-scale KBs because 1) true user distribution is hard to capture and 2) randomly sample training examples from the enormous space would be highly data-inefficient. Instead, we suggest that KBQA models should have three levels of built-in generalization: i.i.d, compositional, and zero-shot. To facilitate the development of KBQA models with stronger generalization, we construct and release a new large-scale, high-quality dataset with 64,331 questions, GrailQA, and provide evaluation settings for all three levels of generalization. In addition, we propose a novel BERT-based KBQA model. The combination of our dataset and model enables us to thoroughly examine and demonstrate, for the first time, the key role of pre-trained contextual embeddings like BERT in the generalization of KBQA.
IBM has unveiled a slew of announcements designed to help businesses scale their use of AI. The company also announced the rollout of new capabilities for its Watson platform. IBM researchers have built a hybrid question-answering system called Neuro-Symbolic-QA (NSQA) that for the first time uses neurosymbolic AI to allow an AI system to offer "and"/ "or" to its recommendations. This will ultimately position the system to perform better in real-world situations, IBM said. "This enhanced reasoning capability comes as a result of an entirely new foundational AI method created by IBM researchers called Logical Neural Networks (LNN), IBM said. LNNs are a modification of today's neural networks so that they become equivalent to a set of logic statements, but they also retain the original learning capability of a neural network, the company explained in a blog post. QA is designed to meet the significant challenges in language-based AI, in particular the fact that the training of NLP ...
During IBM's virtual AI Summit this week, the company announced updates across its Watson family of products in the areas of language, explainability, and workplace automation. A new feature called Reading Comprehension surfaces answers from databases of enterprise documents in response to natural language questions, assigning a confidence score to each response. A novel module in Watson Assistant called FAQ Extraction automatically generates question-and-answer documents. And AI Factsheets automatically captures key facts on a machine learning model's performance and generates reports to "foster transparency and ensure compliance." According to IBM, Reading Comprehension, which was built atop a top-performing question-answering system from IBM Research, is intended to help identify more precise answers in response to queries referring to business documents.
Language is what makes us human. Asking questions is how we learn. Building on the foundations of deep learning and symbolic AI, we have developed technology that can answer complex questions with minimal domain-specific training. Initial results are very encouraging – the system outperforms current state-of-the-art techniques on two prominent datasets with no need for specialized end-to-end training. As this technology matures, it will be possible to use it for better customer support, business intelligence, medical informatics, advanced discovery, and much more.
Airbus AI researchers have developed a system that uses natural language understanding to improve question answering (QA) performance when flight crews search for aircraft operating information. The aerospace industry relies on technical documents such as Aircraft Operating Manuals (AOM), Aircraft Operating Instructions and particularly Flight Crew Operating Manuals (FCOM) to guide flight crews on aircraft operations under normal, abnormal, and emergency conditions. FCOMs are issued by aircraft manufacturers and cover system descriptions, procedures, techniques, and performance data. They are the references used to develop standard operating procedures to improve safety and efficiency. Most government aviation administrations have authorized the use of tablet computers by commercial carrier pilots and flight crews to access FCOM information. The Airbus AI researchers note however that existing electronic flight bag (EFB) systems used for this purpose are in practice little more than pdf viewers with keyword search functionality.
Kapanipathi, Pavan, Abdelaziz, Ibrahim, Ravishankar, Srinivas, Roukos, Salim, Gray, Alexander, Astudillo, Ramon, Chang, Maria, Cornelio, Cristina, Dana, Saswati, Fokoue, Achille, Garg, Dinesh, Gliozzo, Alfio, Gurajada, Sairam, Karanam, Hima, Khan, Naweed, Khandelwal, Dinesh, Lee, Young-Suk, Li, Yunyao, Luus, Francois, Makondo, Ndivhuwo, Mihindukulasooriya, Nandana, Naseem, Tahira, Neelam, Sumit, Popa, Lucian, Reddy, Revanth, Riegel, Ryan, Rossiello, Gaetano, Sharma, Udit, Bhargav, G P Shrivatsa, Yu, Mo
Knowledge base question answering (KBQA) is an important task in Natural Language Processing. Existing approaches face significant challenges including complex question understanding, necessity for reasoning, and lack of large training datasets. In this work, we propose a semantic parsing and reasoning-based Neuro-Symbolic Question Answering(NSQA) system, that leverages (1) Abstract Meaning Representation (AMR) parses for task-independent question under-standing; (2) a novel path-based approach to transform AMR parses into candidate logical queries that are aligned to the KB; (3) a neuro-symbolic reasoner called Logical Neural Net-work (LNN) that executes logical queries and reasons over KB facts to provide an answer; (4) system of systems approach,which integrates multiple, reusable modules that are trained specifically for their individual tasks (e.g. semantic parsing,entity linking, and relationship linking) and do not require end-to-end training data. NSQA achieves state-of-the-art performance on QALD-9 and LC-QuAD 1.0. NSQA's novelty lies in its modular neuro-symbolic architecture and its task-general approach to interpreting natural language questions.
End-to-end question answering (QA) requires both information retrieval (IR) over a large document collection and machine reading comprehension (MRC) on the retrieved passages. Recent work has successfully trained neural IR systems using only supervised question answering (QA) examples from open-domain datasets. However, despite impressive performance on Wikipedia, neural IR lags behind traditional term matching approaches such as BM25 in more specific and specialized target domains such as COVID-19. Furthermore, given little or no labeled data, effective adaptation of QA systems can also be challenging in such target domains. In this work, we explore the application of synthetically generated QA examples to improve performance on closed-domain retrieval and MRC. We combine our neural IR and MRC systems and show significant improvements in end-to-end QA on the CORD-19 collection over a state-of-the-art open-domain QA baseline.
Question Answering (QA) is a popular task in Natural Language Processing and Information Retrieval, in which the goal is to answer a natural language question (going beyond the document retrieval). There are further sub-tasks, for instance, reading comprehension, in which the expected answers can be either a segment of text or span, from the corresponding reading passage of text. The Stanford Question Answering Dataset (SQuAD) [Rajpurkar et al., 2018] is an example of this task. Similarly, another task is Question Answering over Knowledge Bases, in which the expected answer can either be a set of entities in the knowledge base or an answer derived from an aggregation of them. Question Answering over Linked Data (QALD) [Usbeck et al., 2018] and Large Scale Complex Question Answering Dataset (LC-QuAD) [Dubey et al., 2019] are two examples for this task Question or answer type classification plays a key role in question answering [Harabagiu et al., 2000, Allam and Haggag, 2012].