"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.
In our high-speed, multi-tasking culture, fundamental shifts are happening in the way we interact with search technology. Today, mobile devices are the source of 60% of all online searchers. As voice-to-text technology has improved on smartphones and other devices, so has adoption of voice-based commands. In their research recap "Prepare for the Voice Revolution" PWC reports the majority of survey respondents said searching online with voice assistants--like Apple's Siri and Amazon's Alexa--is easier, more convenient, and faster than speaking to a human or texting on a phone. Although younger mobile-first consumers are driving adoption, PWC states, they aren't using the tech as frequently as their 55 counterparts.
When venturing into the field of chatbots and Conversational AI, usually the process starts with a search of what frameworks are available. Invariably this leads you to one of the big cloud Chatbot service providers. Most probably you will end up using IBM Watson Assistant, Microsoft LUIS/Bot Framework, Google Dialog Flow etc. There are advantages…these environments offer easy entry in terms of cost and a low-code or no-code approach. However, one big impediment you often run into with these environments, is the lack of diversity when it comes to language options. This changed 17 June 2021 when IBM introduced the Universal language model.
From search engines to personal assistants, we use question-answering systems every day. When we ask a question ("Where was the painter of the Mona Lisa born?"), the system needs to gather background knowledge ("The Mona Lisa was painted by Leonardo da Vinci", "Leonardo da Vinci was born in Italy") and reason over it to produce the answer ("Italy"). Knowledge sources In recent AI research, such background knowledge is commonly available in the forms of knowledge graphs (KGs) and language models (LMs) pre-trained on a large set of documents. In KGs, entities are represented as nodes and relations between them as edges, e.g. Examples of KGs include Freebase (general-purpose facts)1, ConceptNet (commonsense)2, and UMLS (biomedical facts)3.
Recent advancements in NLP question answering (QA)-based systems have been astonishing. QA systems built on top of the most recent language models (BERT, RoBERTa, etc.) can answer factoid-based questions with relative ease and excellent precision. The task involves finding the relevant document passages containing the answer and extracting the answer by scanning the correct word token span. More challenging QA systems engage with so-called "generative question answering". These systems focus on handling questions where the provided context passages are not simply the source tokens for extracted answers, but provide the larger context to synthesize original answers. Just last week, I was reviewing metric learning, and it occurred to me that it had some similarities with contrastive learning.
Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. In a Final Jeopardy question with the category "colleges and universities," guest host Sanjay Gupta asked contestants which school had recently trademarked the word "The." "In 2019 this public university attempted to trademark the word "The" for use on clothing and hats," the question read. 'JEOPARDY!' EP MIKE RICHARDS SAYS A'ROBUST TEAM' IS SEARCHING FOR A NEW HOST All three contestants got the right answer -- "The" Ohio State University -- prompting viewers to bash it as a question so no-duh, it wasn't even fun.
Alex Migitko started playing tabletop role-playing games (RPGs) 15 years ago. But as life got more demanding, he couldn't commit to the time needed for preparation and play, both as a game facilitator and player. Though passionate about gaming, he ultimately stopped. These "aging out" stories are all too common. Players fall in love with gaming because it provides such depth and breadth of creativity and escape.
One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis. However, existing approaches do not explicitly train QA models on how to resolve the dependency, and thus these models are limited in understanding human dialogues. In this paper, we propose a novel framework, ExCorD (Explicit guidance on how to resolve Conversational Dependency) to enhance the abilities of QA models in comprehending conversational context. ExCorD first generates self-contained questions that can be understood without the conversation history, then trains a QA model with the pairs of original and self-contained questions using a consistency-based regularizer. In our experiments, we demonstrate that ExCorD significantly improves the QA models' performance by up to 1.2 F1 on QuAC, and 5.2 F1 on CANARD, while addressing the limitations of the existing approaches.
Multi-hop logical reasoning is an established problem in the field of representation learning on knowledge graphs (KGs). It subsumes both one-hop link prediction as well as other more complex types of logical queries. Existing algorithms operate only on classical, triple-based graphs, whereas modern KGs often employ a hyper-relational modeling paradigm. In this paradigm, typed edges may have several key-value pairs known as qualifiers that provide fine-grained context for facts. In queries, this context modifies the meaning of relations, and usually reduces the answer set. Hyper-relational queries are often observed in real-world KG applications, and existing approaches for approximate query answering cannot make use of qualifier pairs. In this work, we bridge this gap and extend the multi-hop reasoning problem to hyper-relational KGs allowing to tackle this new type of complex queries. Building upon recent advancements in Graph Neural Networks and query embedding techniques, we study how to embed and answer hyper-relational conjunctive queries. Besides that, we propose a method to answer such queries and demonstrate in our experiments that qualifiers improve query answering on a diverse set of query patterns.
The intelligent question answering (IQA) system can accurately capture users' search intention by understanding the natural language questions, searching relevant content efficiently from a massive knowledge-base, and returning the answer directly to the user. Since the IQA system can save inestimable time and workforce in data search and reasoning, it has received more and more attention in data science and artificial intelligence. This article introduced a domain knowledge graph using the graph database and graph computing technologies from massive heterogeneous data in electric power. It then proposed an IQA system based on the electrical power knowledge graph to extract the intent and constraints of natural interrogation based on the natural language processing (NLP) method, to construct graph data query statements via knowledge reasoning, and to complete the accurate knowledge search and analysis to provide users with an intuitive visualization. This method thoroughly combined knowledge graph and graph computing characteristics, realized high-speed multi-hop knowledge correlation reasoning analysis in tremendous knowledge. The proposed work can also provide a basis for the context-aware intelligent question and answer.
In spoken conversational question answering (SCQA), the answer to the corresponding question is generated by retrieving and then analyzing a fixed spoken document, including multi-part conversations. Most SCQA systems have considered only retrieving information from ordered utterances. However, the sequential order of dialogue is important to build a robust spoken conversational question answering system, and the changes of utterances order may severely result in low-quality and incoherent corpora. To this end, we introduce a self-supervised learning approach, including incoherence discrimination, insertion detection, and question prediction, to explicitly capture the coreference resolution and dialogue coherence among spoken documents. Specifically, we design a joint learning framework where the auxiliary self-supervised tasks can enable the pre-trained SCQA systems towards more coherent and meaningful spoken dialogue learning. We also utilize the proposed self-supervised learning tasks to capture intra-sentence coherence. Experimental results demonstrate that our proposed method provides more coherent, meaningful, and appropriate responses, yielding superior performance gains compared to the original pre-trained language models. Our method achieves state-of-the-art results on the Spoken-CoQA dataset.