Using sequence to sequence algorithms for query expansion has not been explored yet in Information Retrieval literature nor in Question-Answering's. We tried to fill this gap in the literature with a custom Query Expansion engine trained and tested on open datasets. Starting from open datasets, we built a Query Expansion training set using sentence-embeddings-based Keyword Extraction. We therefore assessed the ability of the Sequence to Sequence neural networks to capture expanding relations in the words embeddings' space.
Paraphrase generation is a very close field. Generating new utterances carrying the same meaning expands the initial query and highly increases the robustness of a search-based chatbot @cite_5 @cite_3 .
@cite_5 @cite_3
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This paper proposes a method that leverages multiple machine translation (MT) engines for paraphrase generation (PG). The method includes two stages. Firstly, we use a multi-pivot approach to acquire a set of candidate paraphrases for a source sentence S. Then, we employ two kinds of techniques, namely the selection-based technique and the decoding-based technique, to produce a best paraphrase T for S using the candidates acquired in the first stage. Experimental results show that: (1) The multi-pivot approach is effective for obtaining plenty of valuable candidate paraphrases. (2) Both the selection-based and decoding-based techniques can make good use of the candidates and produce high-quality paraphrases. Moreover, these two techniques are complementary. (3) The proposed method outperforms a state-of-the-art paraphrase generation approach. Paraphrases, which stem from the variety of lexical and grammatical means of expressing meaning available in a language, pose challenges for a sentence generation system. In this paper, we discuss the generation of paraphrases from predicate argument structure using a simple, uniform generation methodology. Central to our approach are lexico-grammatical resources which pair elementary semantic structures with their syntactic realization and a simple but powerful mechanism for combining resources. Most previous work on trainable language generation has focused on two paradigms: (a) using a generation decisions of an existing generator. Both approaches rely on the existence of a handcrafted generation component, which is likely to limit their scalability to new domains. The first contribution of this article is to present Bagel, a fully data-driven generation method that treats the language generation task as a search for the most likely sequence of semantic concepts and realization phrases, according to Factored Language Models (FLMs). As domain utterances are not readily available for most natural language generation tasks, a large creative effort is required to produce the data necessary to represent human linguistic variation for nontrivial domains. This article is based on the assumption that learning to produce paraphrases can be facilitated by collecting data from a large sample of untrained annotators using crowdsourcing—rather than a few domain experts—by relying on a coarse meaning representation. A second contribution of this article is to use crowdsourced data to show how dialogue naturalness can be improved by learning to vary the output utterances generated for a given semantic input. Two data-driven methods for generating paraphrases in dialogue are presented: (a) by sampling from the n-best list of realizations produced by Bagel's FLM reranker; and (b) by learning a structured perceptron predicting whether candidate realizations are valid paraphrases. We train Bagel on a set of 1,956 utterances produced by 137 annotators, which covers 10 types of dialogue acts and 128 semantic concepts in a tourist information system for Cambridge. An automated evaluation shows that Bagel outperforms utterance class LM baselines on this domain. A human evaluation of 600 resynthesized dialogue extracts shows that Bagel's FLM output produces utterances comparable to a handcrafted baseline, whereas the perceptron classifier performs worse. Interestingly, human judges find the system sampling from the n-best list to be more natural than a system always returning the first-best utterance. The judges are also more willing to interact with the n-best system in the future. These results suggest that capturing the large variation found in human language using data-driven methods is beneficial for dialogue interaction. To paraphrase means to rewrite content while preserving the original meaning. Paraphrasing is important in fields such as text reuse in journalism, anonymizing work, and improving the quality of customer-written reviews. This article contributes to paraphrase acquisition and focuses on two aspects that are not addressed by current research: (1) acquisition via crowdsourcing, and (2) acquisition of passage-level samples. The challenge of the first aspect is automatic quality assurance; without such a means the crowdsourcing paradigm is not effective, and without crowdsourcing the creation of test corpora is unacceptably expensive for realistic order of magnitudes. The second aspect addresses the deficit that most of the previous work in generating and evaluating paraphrases has been conducted using sentence-level paraphrases or shorter; these short-sample analyses are limited in terms of application to plagiarism detection, for example. We present the Webis Crowd Paraphrase Corpus 2011 (Webis-CPC-11), which recently formed part of the PAN 2010 international plagiarism detection competition. This corpus comprises passage-level paraphrases with 4067 positive samples and 3792 negative samples that failed our criteria, using Amazon's Mechanical Turk for crowdsourcing. In this article, we review the lessons learned at PAN 2010, and explain in detail the method used to construct the corpus. The empirical contributions include machine learning experiments to explore if passage-level paraphrases can be identified in a two-class classification problem using paraphrase similarity features, and we find that a k-nearest-neighbor classifier can correctly distinguish between paraphrased and nonparaphrased samples with 0.980 precision at 0.523 recall. This result implies that just under half of our samples must be discarded (remaining 0.477 fraction), but our cost analysis shows that the automation we introduce results in a 18p financial saving and over 100 hours of time returned to the researchers when repeating a similar corpus design. On the other hand, when building an unrelated corpus requiring, say, 25p training data for the automated component, we show that the financial outcome is cost neutral, while still returning over 70 hours of time to the researchers. The work presented here is the first to join the paraphrasing and plagiarism communities.