Optimal learning of joint alignments with a faulty oracle

We consider the following problem, which is useful in applications such as joint image and shape alignment. The goal is to recover n discrete variables gi ∈ {0, . . . , k − 1} (up to some global offset) given noisy observations of a set of their pairwise differences {(gi − gj) mod k}; specifically, with probability 1 k + for some > 0 one obtains the correct answer, and with the remaining probability one obtains a uniformly random incorrect answer. We consider a learning-based formulation where one can perform a query to observe a pairwise difference, and the goal is to perform as few queries as possible while obtaining the exact joint alignment. We provide an easy-to-implement, time efficient algorithm that performs O (n lg n k^2 ) queries, and recovers the joint alignment with high probability. We also show that our algorithm is optimal by proving a general lower bound that holds for all non-adaptive algorithms. Our work improves significantly recent work by Chen and Cand´es [CC16], who view the problem as a constrained principal components analysis problem that can be solved using the power method. Specifically, our approach is simpler both in the algorithm and the analysis, and provides additional insights into the problem structure.First author draf

Verifix: Verified Repair of Programming Assignments

Automated feedback generation for introductory programming assignments is useful for programming education. Most works try to generate feedback to correct a student program by comparing its behavior with an instructor's reference program on selected tests. In this work, our aim is to generate verifiably correct program repairs as student feedback. The student assignment is aligned and composed with a reference solution in terms of control flow, and differences in data variables are automatically summarized via predicates to relate the variable names. Failed verification attempts for the equivalence of the two programs are exploited to obtain a collection of maxSMT queries, whose solutions point to repairs of the student assignment. We have conducted experiments on student assignments curated from a widely deployed intelligent tutoring system. Our results indicate that we can generate verified feedback in up to 58% of the assignments. More importantly, our system indicates when it is able to generate a verified feedback, which is then usable by novice students with high confidence

OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization

The performance of automatic summarization models has improved dramatically in recent years. Yet, there is still a gap in meeting specific information needs of users in real-world scenarios, particularly when a targeted summary is sought, such as in the useful aspect-based summarization setting targeted in this paper. Previous datasets and studies for this setting have predominantly concentrated on a limited set of pre-defined aspects, focused solely on single document inputs, or relied on synthetic data. To advance research on more realistic scenarios, we introduce OpenAsp, a benchmark for multi-document \textit{open} aspect-based summarization. This benchmark is created using a novel and cost-effective annotation protocol, by which an open aspect dataset is derived from existing generic multi-document summarization datasets. We analyze the properties of OpenAsp showcasing its high-quality content. Further, we show that the realistic open-aspect setting realized in OpenAsp poses a challenge for current state-of-the-art summarization models, as well as for large language models.Comment: EMNLP 202

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

Learning with Joint Inference and Latent Linguistic Structure in Graphical Models

Constructing end-to-end NLP systems requires the processing of many types of linguistic information prior to solving the desired end task. A common approach to this problem is to construct a pipeline, one component for each task, with each system\u27s output becoming input for the next. This approach poses two problems. First, errors propagate, and, much like the childhood game of telephone , combining systems in this manner can lead to unintelligible outcomes. Second, each component task requires annotated training data to act as supervision for training the model. These annotations are often expensive and time-consuming to produce, may differ from each other in genre and style, and may not match the intended application. In this dissertation we present a general framework for constructing and reasoning on joint graphical model formulations of NLP problems. Individual models are composed using weighted Boolean logic constraints, and inference is performed using belief propagation. The systems we develop are composed of two parts: one a representation of syntax, the other a desired end task (semantic role labeling, named entity recognition, or relation extraction). By modeling these problems jointly, both models are trained in a single, integrated process, with uncertainty propagated between them. This mitigates the accumulation of errors typical of pipelined approaches. Additionally we propose a novel marginalization-based training method in which the error signal from end task annotations is used to guide the induction of a constrained latent syntactic representation. This allows training in the absence of syntactic training data, where the latent syntactic structure is instead optimized to best support the end task predictions. We find that across many NLP tasks this training method offers performance comparable to fully supervised training of each individual component, and in some instances improves upon it by learning latent structures which are more appropriate for the task