We will see what BLEU algorithm is and how it can be used in recognition of entailments. Basically, the algorithm looks for n-gram coincidences between a candidate text the automatically produced translation and a set of reference texts the human-made translations. The pseudo code of BLEU is as follows: For several values of n typically from 1 to 4calculate the percentage of n- grams from the candidate translation that appears in any of the human translations.
The frequency of each n-gram is limited to the maximum frequency with which it appears in any reference. Combine the values obtained for each value of n, as a weighted linear average. Apply a brevity factor to penalize short candidate texts which may have n-grams in common with the references, but may be incomplete.
If the candidate is shorter than the references, this factor is calculated as the ratio between the length of the candidate text and the length of the reference which has the most similar length. It can be seen from this pseudo code that BLEU is not only a keyword matching method between pairs of texts.
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It takes into account several other factors that make it more robust: It calculates the length of the text in comparison with the length of reference texts. If the candidate text is shorter than the reference texts, this is considered to be an indicative of a poor quality translation and thus, BLEU penalizes it. The measure of similarity can be considered csd engineers swiss anti aging a precision value that calculates 14 Bilingual Evaluation Understudy how many of the n-grams from the candidate appear in the reference texts.
The final score is the result of the weighted sum of the logarithms of the different values of the precision, csd engineers swiss anti aging n varying from 1 to 4.
It is not interesting to try higher values of n since coincidences longer than four-grams are very unusual. This value indicates how similar the candidate and reference texts are. In fact, the closer the jeet kune do unlimited switzerland anti aging is to 1, the more similar they are.
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Papineni et al. A variant of this algorithm has also been applied to evaluate text summarization systems Lin and Hovy, and to help in the assessment of open-ended questions Alfonseca and Pérez, Once the algorithm is applied, they had seen that the results confirm the use of BLEU as baseline for the automatic recognition of textual entailments.
In order to recognize entailments using BLEU, the first decision is to choose whether the candidate text should be considered as part of the entailment T or as the hypothesis H. In order to make this choice, they did a first experiment in which they considered the T part as the reference and the H as the candidate.
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The output of their algorithm which uses BLEU was taken as the confidence score and it was also used to give the final answer to each entailment pair. They performed an optimization procedure for the development set that chose the best threshold according to the percentage of success of correctly recognized entailments.
The value obtained was 0. The result of 0.
In the following competitions the n-gram word similarity method was very popular among the participating systems 6 systems in11 systems in and over 14 in Objects to be matched two images, patterns, text and hypothesis in RTE task, etc. Thus, following Dagan and Glickman,since the hypothesis H and text T may be represented by two syntactic graphs, the textual entailment recognition problem can be reduced to graph similarity measure estimation, although textual entailment has particular properties Pazienza et al.
The authors Pazienza et al. T semantically subsumes H e. T syntactically subsumes H e. T directly implies H e.
Все, кто приписан к Носителю, - продолжил Орел, - должны немедленно приступить к сборам и завершить их еще до обеда. Но те, кто определен в Узел, в случае нежелания могут изменить место своего назначения. Сегодня, после того как все приписанные к Носителю перейдут на свой корабль, я жду в кафетерии тех, кто хочет вместо Узла отправиться на Носитель.
Constituents are lexicalized syntactic trees with explicit syntactic heads and potential semantic governors gov. Dependencies in D represent typed and ambiguous relations among a constituent, the head, and one of its modifiers.
Ambiguity is represented using plausibility between 0 and 1. They work under two simplifying assumptions: H is supposed to be a sentence completely describing a fact in an assertive or negative way and H should be a simple S-V-O sentence subject, verb, object order.
Linguistic transformations such as nominalization, passivization, terapie cu lumină anti-îmbătrânire de lux argument movementas well as negation, must also be considered, since they can play a very important role. The problem is to extract the maximal subgraph of XDGT that is in a subgraph isomorphism relation with XDGH, through the definition of two functions fC over nodes and fD over edges Pazienza et al, This is possible if the selection process of the subsets of the graphs nodes guarantees the possibility of defining the function fC.
If this is done, the bijective function fC is derived by construction.
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The mapping process is based on the notion of anchors. The set of anchors A for an entailment pair contains an anchor for each of the hypothesis constituents having correspondences in the text T. For example in the entailment pair shown below, propositions: 18 Figure 1: Example of entailment from Pazienza et al.
Syntactic similarity, defined by fD, will capture how much similar the syntactic structure accompanied is to the two constituents i. Both semantic and syntactic similarity derived respectively from fC and fD must be taken into consideration to evaluate the overall graph similarity measure, as the former captures the notion of node subsumption, and the latter the notion of edge subsumption. The method is more sophisticated in comparison with the BLUE approach, and it considers both syntactic and semantic levels.
Initial, in RTE-1, this method was completed with different alignment methods and matching algorithms and used by many groups in the following challenges, those of Katrenko and Adriaans, and Zanzotto et al. Tree Edit Distance Algorithms The core of this approach Kouylekov and Magnini, is a tree edit distance algorithm applied on the dependency trees of both the text and the hypothesis.
If the distance i. The authors designed a system based on the intuition that the probability of an entailment relation between T and H is related to the ability to show that the whole content of H can be mapped into the content of T. The more straightforward the mapping establishment can be, the more probable the entailment relation is.
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Since a mapping can be described as csd engineers swiss anti aging sequence of editing operations needed to transform T into H, where each edit operation has a cost associated with it, they assign an entailment relation if the overall cost of the transformation is below a 19 certain threshold, empirically estimated on the training data.
According to their approach, T entails H if we have a sequence of transformations applied to T such that we can obtain H with an overall cost below a certain threshold. The underlying assumption is that pairs between which an entailment relation holds have a low cost of transformation. The transformations types i.
The authors have implemented the tree edit distance algorithm described in Zhang and Shasha, and applied it to the dependency trees derived from T and H.
Edit operations are defined at the single nodes level of the dependency tree i. Since the Zhang and Shasha, algorithm does not consider labels on edges, while dependency trees provide them, each dependency relation R from a node A to a node B has been re-written as a complex label B-R concatenating the name of the destination node and the name of the relation. All nodes except for the root of the tree are relabelled in such way.
The algorithm is directional: the aim is to find the best sequence i. According to the constraints described above, the following transformations are allowed: Insertion: insert a node from the dependency tree of H into the dependency tree of T. When a node is inserted, it is attached to the dependency relation of the source label.
Deletion: delete a node N from the dependency tree of T. When N is deleted, all its children are attached to the parent of N. It is not required to explicitly delete the children of N as they are going to be either deleted or substituted on a following step. Substitution: change the label of a node N1 in the source tree into a label of a node N2 of the target tree. Substitution is allowed only if the two nodes share the same part-of-speech.
In case of substitution, the relation attached to the substituted node is changed with the relation of the new node. The initial approach used by Kouylekov and Magnini, determined, based on the distance between trees, the final answer for the current pair.
It should be noticed that this system does not use external resources like WordNet, paraphrases collection, or resources with named entities or acronyms. The next systems were more complex and combined the initial approach with machine learning algorithms Kozareva, Montoyo, or used the probabilistic transformations of the trees Harmeling,