Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)

A Unified Kernel Approach For Learning Typed Sentence Rewritings

International audienceMany high level natural language processing problems can be framed as determining if two given sentences are a rewriting of each other. In this paper, we propose a class of kernel functions, referred to as type-enriched string rewriting kernels, which, used in kernel-based machine learning algorithms, allow to learn sentence rewritings. Unlike previous work, this method can be fed external lexical semantic relations to capture a wider class of rewriting rules. It also does not assume preliminary syntactic parsing but is still able to provide a unified framework to capture syntactic structure and alignments between the two sentences. We experiment on three different natural sentence rewriting tasks and obtain state-of-the-art results for all of them

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

Analyzing short-answer questions and their automatic scoring - studies on semantic relations in reading comprehension and the reduction of human annotation effort

Short-answer questions are a wide-spread exercise type in many educational areas. Answers given by learners to such questions are scored by teachers based on their content alone ignoring their linguistic correctness as far as possible. They typically have a length of up to a few sentences. Manual scoring is a time-consuming task, so that automatic scoring of short-answer questions using natural language processing techniques has become an important task. This thesis focuses on two aspects of short-answer questions and their scoring: First, we concentrate on a reading comprehension scenario for learners of German as a foreign language, where students answer questions about a reading text. Within this scenario, we examine the multiple relations between reading texts, learner answers and teacher-specified target answers. Second, we investigate how to reduce human scoring workload by both fully automatic and computer-assisted scoring. The latter is a scenario where scoring is not done entirely automatically, but where a teacher receives scoring support, for example, by means of clustering similar answers together. Addressing the first aspect, we conduct a series of corpus annotation studies which highlight the relations between pairs of learner answers and target answers, as well as between both types of answers and the reading text they refer to. We annotate sentences from the reading text that were potentially used by learners or teachers for constructing answers and observe that, unsurprisingly, most correct answers can easily be linked to the text; incorrect answers often link to the text as well, but are often backed up by a part of the text not relevant to answer the question. Based on these findings, we create a new baseline scoring model which considers for correctness whether learners looked for an answer in the right place or not. After identifying those links into the text, we label the relation between learner answers and target answers as well as between reading texts and answers by annotating entailment relations. In contrast to the widespread assumption that scoring can be fully mapped to the task of recognizing textual entailment, we find the two tasks to be only closely related and not completely equivalent. Correct answers do often, but not always, entail the target answer, as well as part of the related text, and incorrect answers do most of the time not stand in an entailment relation to the target answer, but often have some overlap with the text. This close relatedness allows us to use gold-standard entailment information to improve the performance of automatic scoring. We also use links between learner answers and both reading texts and target answers in a statistical alignment-based scoring approach using methods from machine translation and reach a performance comparable to an existing knowledge-based alignment approach. Our investigations into how human scoring effort can be reduced when learner answers are manually scored by teachers are based on two methods: active learning and clustering. In the active learning approach, we score particularly informative items first, i.e., items from which a classifier can learn most, identifying them using uncertainty-based sample selection. In this way, we reach a higher performance with a given number of annotation steps compared to randomly selected answers. In the second research strand, we use clustering methods to group similar answers together, such that groups of answers can be scored in one scoring step. In doing so, the number of necessary labeling steps can be substantially reduced. When comparing clustering-based scoring to classical supervised machine learning setups, where the human annotations are used to train a classifier, supervised machine learning is still in the lead in terms of performance, whereas clusters provide the advantage of structured output. However, we are able to close part of the performance gap by means of supervised feature selection and semi-supervised clustering. In an additional study, we investigate the automatic processing of learner language with respect to the performance of part-of-speech (POS) tagging tools. We manually annotate a German reading comprehension corpus both with spelling normalization and POS information and find that the performance of automatic POS tagging can be improved by spell-checking the data using the reading text as additional evidence for lexical material intended in a learner answer.Short-Answer-Fragen sind ein weit verbreiteter Aufgabentyp in vielen Bildungsbereichen. Die Antworten, die Lerner zu solchen Aufgaben geben, werden von Lehrenden allein auf Grundlage ihres Inhalts bewertet; linguistische Korrektheit wird soweit möglich ignoriert. Diese Doktorarbeit legt ihren Schwerpunkt auf zwei Aspekte im Zusammenhang mit Short- Answer-Fragen und ihrer Bewertung: Zum einen betrachten wir ein Leseverständnisszenario, bei dem Studenten Fragen zu Lesetexten beantworten. Dabei untersuchen wir insbesondere die verschiedenen Beziehungen, die es zwischen Lesetexten, Lernerantworten und vom Lehrer erstellten Musterantworten gibt. Zum anderen untersuchen wir, wie der menschliche Bewertungsaufwand durch voll-automatisches und computergestütztes Bewerten reduziert werden kann. Bei letzterem handelt es sich um ein Szenario, in dem Lehrer bei der Bewertung unterstützt werden, z.B. indem ähnliche Antworten automatisch gruppiert werden. Zur Untersuchung des ersten Aspekts unternehmen wir eine Reihe von Korpusannotationsstudien, die sowohl die Beziehungen zwischen Lerner- und Musterantworten beleuchten, als auch die Beziehung zwischen diesen Antworten und dem Lesetext, auf den sie sich beziehen. Wir annotieren Sätze aus dem Lesetext, die vermutlich bei der Formulierung einer Antwort benutzt wurden und machen die zu erwartende Beobachtung, dass die meisten korrekten Antworten problemlos mit bestimmten Textpassagen in Verbindung gebracht werden können. Inkorrekte Antworten haben ebenfalls oft eine Verbindung zu bestimmten Textpassagen, die aber oft für die jeweilige Frage nicht relevant sind. Auf Grundlage dieser Erkenntnisse entwerfen wir ein neues Baseline-Bewertungsmodell, das für die Korrektheit einer Antwort nur in Betracht zieht, ob der Lerner die Antwort an der richtigen Stelle im Lesetext gesucht hat oder nicht. Nachdem wir diese Verbindungen in den Text identifiziert haben, annotieren wir die Relation zwischen Lerner- und Musterantworten und zwischen Texten und Antworten mit Entailment- Relationen. Im Gegensatz zur der weitverbreiteten Annahme, dass das Bewerten von Short- Answer-Fragen und das Erkennen von Textual-Entailment-Relationen zwischen Lerner und Musterantworten sich direkt entsprechen, finden wir heraus, dass die beiden Aufgaben nur nahe verwandt aber nicht vollständig äquivalent sind. Korrekte Antworten entailen meistens, aber nicht immer, die Musterantwort und auch den entsprechenden Satz im Lesetext. Inkorrekte Antworten stehen meist in keiner Entailmentrelation mit der Musterantwort, haben aber oft zumindest teilweisen Overlap mit dem Text. Diese nahe Verwandtschaft erlaubt es uns, Goldstandard-Entailmentinformation zu benutzen, um die Performanz beim automatischen Bewerten zu verbessern. Wir benutzen die annotierten Verbindungen zwischen Lesetexten und Antworten auch in einem Scoringansatz, der auf statistischem Alignment basiert und Methoden aus dem Bereich der maschinellen Übersetzung nutzt. Dabei erreichen wir eine Scoringgenauigkeit, die mit Ansätzen, die ein existierendes wissensbasiertes Alignment nutzen, vergleichbar ist. Unsere Untersuchungen, wie der Bewertungsaufwand beim Menschen verringert werden kann, wenn Antworten vom Lehrer manuell bewertet werden, basieren auf zwei Methoden: Active Learning und Clustering. Beim Active-Learning-Ansatz werden besonders informative Antworten vorrangig zur Bewertung ausgewählt, d.h. solche Antworten, von denen ein Klassifikator besonders viel lernen kann. Wir identifizieren solche Antworten durch Uncertainty-Sampling- Methoden und erreichen dadurch mit einer gegebenen Anzahl von Annotationsschritten eine höhere Klassifikationsgenauigkeit als mit zufällig ausgewählten Antworten. In unserem zweiten Forschungszweig nutzen wir Clusteringmethoden um ähnliche Antworten zu gruppieren, so dass Gruppen von Antworten in einem Annotationsschritt bewertet werden können. Dadurch kann die Anzahl der insgesamt nötigen Bewertungsschritte drastisch reduziert werden. Beim Vergleich zwischen clusteringbasierten Bewertungsverfahren und klassischem überwachten maschinellen Lernen, bei dem menschliche Annotationen dazu genutzt werden, einen Klassifikator zu trainieren, erbringen überwachte maschinelle Lernverfahren immer noch eine höhere Bewertungsgenauigkeit. Demgegenüber bringen Cluster den Vorteil eines strukturierten Outputs mit sich. Wir sind jedoch in der Lage, einen Teil diese Genauigkeitslücke zu schließen, in dem wir überwachte Featureauswahl und halbüberwachtes Clustering anwenden. In einer zusätzlichen Studie untersuchen wir die automatische Verarbeitung von Lernersprache im Hinblick auf die Performanz vonWerkzeugen für dasWortarten-Tagging. Wir annotieren ein deutsches Leseverstehenskorpus manuell sowohl mit Normalisierungsinformation in Bezug auf Rechtschreibung als auch mit Wortartinformation. Als Ergebnis der Studie finden wir, dass die Performanz bei der automatischen Wortartenzuweisung durch Rechtschreibkorrektur verbessert werden kann, insbesondere wenn wir den Lesetext als zusätzliche Evidenz dafür verwenden, welche Wörter der Leser in einer Antwort vermutlich benutzen wollte

Can Knowledge Rich Sentences Help Language Models To Solve Common Sense Reasoning Problems?

abstract: Significance of real-world knowledge for Natural Language Understanding(NLU) is well-known for decades. With advancements in technology, challenging tasks like question-answering, text-summarizing, and machine translation are made possible with continuous efforts in the field of Natural Language Processing(NLP). Yet, knowledge integration to answer common sense questions is still a daunting task. Logical reasoning has been a resort for many of the problems in NLP and has achieved considerable results in the field, but it is difficult to resolve the ambiguities in a natural language. Co-reference resolution is one of the problems where ambiguity arises due to the semantics of the sentence. Another such problem is the cause and result statements which require causal commonsense reasoning to resolve the ambiguity. Modeling these type of problems is not a simple task with rules or logic. State-of-the-art systems addressing these problems use a trained neural network model, which claims to have overall knowledge from a huge trained corpus. These systems answer the questions by using the knowledge embedded in their trained language model. Although the language models embed the knowledge from the data, they use occurrences of words and frequency of co-existing words to solve the prevailing ambiguity. This limits the performance of language models to solve the problems in common-sense reasoning task as it generalizes the concept rather than trying to answer the problem specific to its context. For example, "The painting in Mark's living room shows an oak tree. It is to the right of a house", is a co-reference resolution problem which requires knowledge. Language models can resolve whether "it" refers to "painting" or "tree", since "house" and "tree" are two common co-occurring words so the models can resolve "tree" to be the co-reference. On the other hand, "The large ball crashed right through the table. Because it was made of Styrofoam ." to resolve for "it" which can be either "table" or "ball", is difficult for a language model as it requires more information about the problem. In this work, I have built an end-to-end framework, which uses the automatically extracted knowledge based on the problem. This knowledge is augmented with the language models using an explicit reasoning module to resolve the ambiguity. This system is built to improve the accuracy of the language models based approaches for commonsense reasoning. This system has proved to achieve the state of the art accuracy on the Winograd Schema Challenge.Dissertation/ThesisMasters Thesis Computer Science 201

An Introduction to String Re-Writing Kernel

Abstract Learning for sentence re-writing is a fundamental task in natural language processing and information retrieval. In this paper, we propose a new class of kernel functions, referred to as string rewriting kernel, to address the problem. A string re-writing kernel measures the similarity between two pairs of strings. It can capture the lexical and structural similarity between sentence pairs without the need of constructing syntactic trees. We further propose an instance of string re-writing kernel which can be computed efficiently. Experimental results on benchmark datasets show that our method can achieve comparable results with state-of-the-art methods on two sentence re-writing learning tasks: paraphrase identification and recognizing textual entailment

Performance-oriented dependency parsing

In the last decade a lot of dependency parsers have been developed. This book describes the motivation for the development of yet another parser - MDParser. The state of the art is presented and the deficits of the current developments are discussed. The main problem of the current parsers is that the task of dependency parsing is treated independently of what happens before and after it. However, in practice parsing is rarely done for the sake of parsing itself, but rather in order to use the results in a follow-up application. Additionally, current parsers are accuracy-oriented and focus only on the quality of the results, neglecting other important properties, especially efficiency. The evaluation of some NLP technologies is sometimes as difficult as the task itself. For dependency parsing it was long thought not to be the case, however, some recent works show that the current evaluation possibilities are limited. This book proposes a methodology to account for the weaknesses and combine the strengths of the current approaches. Finally, MDParser is evaluated against other state-of-the-art parsers. The results show that it is the fastest parser currently available and it is able to process plain text, which other parsers usually cannot. The results are slightly behind the top accuracies in the field, however, it is demonstrated that it is not decisive for applications

Performance-oriented dependency parsing

In the last decade a lot of dependency parsers have been developed. This book describes the motivation for the development of yet another parser - MDParser. The state of the art is presented and the deficits of the current developments are discussed. The main problem of the current parsers is that the task of dependency parsing is treated independently of what happens before and after it. However, in practice parsing is rarely done for the sake of parsing itself, but rather in order to use the results in a follow-up application. Additionally, current parsers are accuracy-oriented and focus only on the quality of the results, neglecting other important properties, especially efficiency. The evaluation of some NLP technologies is sometimes as difficult as the task itself. For dependency parsing it was long thought not to be the case, however, some recent works show that the current evaluation possibilities are limited. This book proposes a methodology to account for the weaknesses and combine the strengths of the current approaches. Finally, MDParser is evaluated against other state-of-the-art parsers. The results show that it is the fastest parser currently available and it is able to process plain text, which other parsers usually cannot. The results are slightly behind the top accuracies in the field, however, it is demonstrated that it is not decisive for applications