The first Answer Validation Exercise (AVE) has been launched at the Cross Language Evaluation Forum 2006. This task is aimed at developing systems able to decide whether the answer of a Question Answering system is correct or not. The exercise is described here together with the evaluation methodology and the systems results. The starting point for the AVE 2006 was the reformulation of the Answer Validation as a Recognizing Textual Entailment problem, under the assumption that hypothesis can be automatically generated instantiating hypothesis patterns with the QA systems' answers. 11 groups have participated with 38 runs in 7 different languages. Systems that reported the use of logic have obtained the best results in their respective subtasks.

The general aim of the third CLEF Multilingual Question Answering Track was to set up a common and replicable evaluation framework to test both monolingual and cross-language Question Answering (QA) systems that process queries and documents in several European languages. Nine target languages and ten source languages were exploited to enact 8 monolingual and 73 cross-language tasks. Twenty-four groups participated in the exercise.Overall results showed a general increase in performance in comparison to last year. The best performing monolingual system irrespective of target language answered 64.5% of the questions correctly (in the monolingual Portuguese task), while the average of the best performances for each target language was 42.6%. The cross-language step instead entailed a considerable drop in performance. In addition to accuracy, the organisers also measured the relation between the correctness of an answer and a system's stated confidence in it, showing that the best systems did not always provide the most reliable confidence score.

This paper reports on the pilot question answering track that was carried out within the CLEF initiative this year. The track was divided into monolingual and bilingual tasks: monolingual systems were evaluated within the frame of three non-English European languages, Dutch, Italian and Spanish, while in the crosslanguage tasks an English document collection constituted the target corpus for Italian, Spanish, Dutch, French and German queries. Participants were given 200 questions for each task, and were allowed to submit up to two runs per task with up to three responses (either exact answers or 50 bytes long strings) per question. We give here an overview of the track: we report on each task and discuss the creation of the multilingual test sets and the participants' results.

The Recognizing Textual Entailment System shown here is based on the use of a broad-coverage parser to extract dependency relationships; in addition, WordNet relations are used to recognize entailment at the lexical level. The work investigates whether the mapping of dependency trees from text and hypothesis give better evidence of entailment than the matching of plain text alone. While the use of WordNet seems to improve system's performance, the notion of mapping between trees here explored (inclusion) shows no improvement, suggesting that other notions of tree mappings should be explored such as tree edit distances or tree alignment distances.

This paper describes the methodology for testing the performance of Machine Reading systems through Question Answering and Reading Comprehension Tests. This was the attempt of the QA4MRE challenge which was run as a Lab at CLEF 2011-2013. The traditional QA task was replaced by a new Machine Reading task, whose intention was to ask questions that required a deep knowledge of individual short texts and in which systems were required to choose one answer, by analysing the corresponding test document in conjunction with background text collections provided by the organization. Four different tasks have been organized during these years: Main Task, Processing Modality and Negation for Machine Reading, Machine Reading of Biomedical Texts about Alzheimer's disease, and Entrance Exams. This paper describes their motivation, their goals, their methodology for preparing the data sets, their background collections, their metrics used for the evaluation, and the lessons learned along these three years.

The fifth QA campaign at CLEF [1], having its first edition in 2003, offered not only a main task but an Answer Validation Exercise (AVE) [2], which continued last year's pilot, and a new pilot: the Question Answering on Speech Transcripts (QAST) [3, 15]. The main task was characterized by the focus on cross-linguality, while covering as many European languages as possible. As novelty, some QA pairs were grouped in clusters. Every cluster was characterized by a topic (not given to participants). The questions from a cluster possibly contain co-references between one of them and the others. Finally, the need for searching answers in web formats was satisfied by introducing Wikipedia 1 as document corpus. The results and the analyses reported by the participants suggest that the introduction of Wikipedia and the topic related questions led to a drop in systems' performance.

The fifth QA campaign at CLEF [1], having its first edition in 2003, offered not only a main task but an Answer Validation Exercise (AVE) [2], which continued last year's pilot, and a new pilot: the Question Answering on Speech Transcripts (QAST) [3, 15]. The main task was characterized by the focus on cross-linguality, while covering as many European languages as possible. As novelty, some QA pairs were grouped in clusters. Every cluster was characterized by a topic (not given to participants). The questions from a cluster possibly contain co-references between one of them and the others. Finally, the need for searching answers in web formats was satisfied by introducing Wikipedia 1 as document corpus. The results and the analyses reported by the participants suggest that the introduction of Wikipedia and the topic related questions led to a drop in systems' performance.

The general aim of the third CLEF Multilingual Question Answering Track was to set up a common and replicable evaluation framework to test both monolingual and cross-language Question Answering (QA) systems that process queries and documents in several European languages. Nine target languages and ten source languages were exploited to enact 8 monolingual and 73 cross-language tasks. Twenty-four groups participated in the exercise.Overall results showed a general increase in performance in comparison to last year. The best performing monolingual system irrespective of target language answered 64.5% of the questions correctly (in the monolingual Portuguese task), while the average of the best performances for each target language was 42.6%. The cross-language step instead entailed a considerable drop in performance. In addition to accuracy, the organisers also measured the relation between the correctness of an answer and a system's stated confidence in it, showing that the best systems did not always provide the most reliable confidence score.