Control in Hybrid Chatbots

Customer data typically is held in database systems, which can be seen as rule-based knowledge base, whereas businesses increasingly want to benefit from the capabilities of large, pre-trained language models. In this technical report, we describe a case study of how a commercial rule engine and an integrated neural chatbot may be integrated, and what level of control that particular integration mode leads to. We also discuss alternative ways (including past ways realized in other systems) how researchers strive to maintain control and avoid what has recently been called model "hallucination".Comment: 12 pages, 3 figure

Cross-lingual Question Answering with QED

We present improvements and modifications of the QED open-domain question answering system developed for TREC-2003 to make it cross-lingual for participation in the CrossLinguistic Evaluation Forum (CLEF) Question Answering Track 2004 for the source languages French and German and the target language English. We use rule-based question translation extended with surface pattern-oriented pre- and post-processing rules for question reformulation to create and English query from its French or German original. Our system uses deep processing for the question and answers, which requires efficient and radical prior search space pruning. For answering factoid questions, we report an accuracy of 16% (German to English) and 20% (French to English), respectively

a comparison of two paraphrase models for taxonomy augmentation

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attr2vec jointly learning word and contextual attribute embeddings with factorization machines

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Generating Annotated Corpora for Reading Comprehension and Question Answering Evaluation

Recently, reading comprehension tests for students and adult language learners have received increased attention within the NLP community as a means to develop and evaluate robust question answering (NLQA) methods. We present our ongoing work on automatically creating richly annotated corpus resources for NLQA and on comparing automatic methods for answering questions against this data set. Starting with the CBC4Kids corpus, we have added XML annotation layers for tokenization, lemmatization, stemming, semantic classes, POS tags and bestranking syntactic parses to support future experiments with semantic answer retrieval and inference. Using this resource, we have calculated a baseline for word-overlap based answer retrieval (Hirschman et al., 1999) on the CBC4Kids data and found the method performs slightly better than on the REMEDIA corpus. We hope that our richly annotated version of the CBC4Kids corpus will become a standard resource, especially as a controlled environment for evaluating inference-based techniques

A Framework for Text Mining Services

The growth of online scientific literature, coupled with the growing maturity of text processing technology, has boosted the importance of text mining as a potentially crucial tool. However, there are several challenges to be addressed before sophisticated text mining services can be deployed within emerging workflow environments. Our work contributes at two levels. At the invocation level, we have developed a flexible XML-based pipeline architecture which allows non-XML processors to be readily integrated. At the description/discovery level, we have developed a broker for service composition, and an accompanying domain ontology, that leverage the OWL-S approach to service profiles.

QED: The Edinburgh TREC-2003 Question Answering System

This report describes a new open-domain answer retrieval system developed at the University of Edinburgh and gives results for the TREC-12 question answering track. Phrasal answers are identified by increasingly narrowing down the search space from a large text collection to a single phrase. The system uses document retrieval, query-based passage segmentation and ranking, semantic analysis from a wide-coverage parser, and a unification-like matching procedure to extract potential answers. A simple Web-based answer validation stage is also applied. The system is based on the Open Agent Architecture and has a parallel design so that multiple questions can be answered simultaneously on a Beowulf cluster