Automatic annotation of error types for grammatical error correction

Grammatical Error Correction (GEC) is the task of automatically detecting and correcting grammatical errors in text. Although previous work has focused on developing systems that target specific error types, the current state of the art uses machine translation to correct all error types simultaneously. A significant disadvantage of this approach is that machine translation does not produce annotated output and so error type information is lost. This means we can only evaluate a system in terms of overall performance and cannot carry out a more detailed analysis of different aspects of system performance. In this thesis, I develop a system to automatically annotate parallel original and corrected sentence pairs with explicit edits and error types. In particular, I first extend the Damerau- Levenshtein alignment algorithm to make use of linguistic information when aligning parallel sentences, and supplement this alignment with a set of merging rules to handle multi-token edits. The output from this algorithm surpasses other edit extraction approaches in terms of approximating human edit annotations and is the current state of the art. Having extracted the edits, I next classify them according to a new rule-based error type framework that depends only on automatically obtained linguistic properties of the data, such as part-of-speech tags. This framework was inspired by existing frameworks, and human judges rated the appropriateness of the predicted error types as ‘Good’ (85%) or ‘Acceptable’ (10%) in a random sample of 200 edits. The whole system is called the ERRor ANnotation Toolkit (ERRANT) and is the first toolkit capable of automatically annotating parallel sentences with error types. I demonstrate the value of ERRANT by applying it to the system output produced by the participants of the CoNLL-2014 shared task, and carry out a detailed error type analysis of system performance for the first time. I also develop a simple language model based approach to GEC, that does not require annotated training data, and show how it can be improved using ERRANT error types

Parsing and Evaluation. Improving Dependency Grammars Accuracy. Anàlisi Sintàctica Automàtica i Avaluació. Millora de qualitat per a Gramàtiques de Dependències

Because parsers are still limited in analysing specific ambiguous constructions, the research presented in this thesis mainly aims to contribute to the improvement of parsing performance when it has knowledge integrated in order to deal with ambiguous linguistic phenomena. More precisely, this thesis intends to provide empirical solutions to the disambiguation of prepositional phrase attachment and argument recognition in order to assist parsers in generating a more accurate syntactic analysis. The disambiguation of these two highly ambiguous linguistic phenomena by the integration of knowledge about the language necessarily relies on linguistic and statistical strategies for knowledge acquisition. The starting point of this research proposal is the development of a rule-based grammar for Spanish and for Catalan following the theoretical basis of Dependency Grammar (Tesnière, 1959; Mel’čuk, 1988) in order to carry out two experiments about the integration of automatically- acquired knowledge. In order to build two robust grammars that understand a sentence, the FreeLing pipeline (Padró et al., 2010) has been used as a framework. On the other hand, an eclectic repertoire of criteria about the nature of syntactic heads is proposed by reviewing the postulates of Generative Grammar (Chomsky, 1981; Bonet and Solà, 1986; Haegeman, 1991) and Dependency Grammar (Tesnière, 1959; Mel’čuk, 1988). Furthermore, a set of dependency relations is provided and mapped to Universal Dependencies (Mcdonald et al., 2013). Furthermore, an empirical evaluation method has been designed in order to carry out both a quantitative and a qualitative analysis. In particular, the dependency parsed trees generated by the grammars are compared to real linguistic data. The quantitative evaluation is based on the Spanish Tibidabo Treebank (Marimon et al., 2014), which is large enough to carry out a real analysis of the grammars performance and which has been annotated with the same formalism as the grammars, syntactic dependencies. Since the criteria between both resources are differ- ent, a process of harmonization has been applied developing a set of rules that automatically adapt the criteria of the corpus to the grammar criteria. With regard to qualitative evaluation, there are no available resources to evaluate Spanish and Catalan dependency grammars quali- tatively. For this reason, a test suite of syntactic phenomena about structure and word order has been built. In order to create a representative repertoire of the languages observed, descriptive grammars (Bosque and Demonte, 1999; Solà et al., 2002) and the SenSem Corpus (Vázquez and Fernández-Montraveta, 2015) have been used for capturing relevant structures and word order patterns, respectively. Thanks to these two tools, two experiments have been carried out in order to prove that knowl- edge integration improves the parsing accuracy. On the one hand, the automatic learning of lan- guage models has been explored by means of statistical methods in order to disambiguate PP- attachment. More precisely, a model has been learned with a supervised classifier using Weka (Witten and Frank, 2005). Furthermore, an unsupervised model based on word embeddings has been applied (Mikolov et al., 2013a,b). The results of the experiment show that the supervised method is limited in predicting solutions for unseen data, which is resolved by the unsupervised method since provides a solution for any case. However, the unsupervised method is limited if it Parsing and Evaluation Improving Dependency Grammars Accuracy only learns from lexical data. For this reason, training data needs to be enriched with the lexical value of the preposition, as well as semantic and syntactic features. In addition, the number of patterns used to learn language models has to be extended in order to have an impact on the grammars. On the other hand, another experiment is carried out in order to improve the argument recog- nition in the grammars by the acquisition of linguistic knowledge. In this experiment, knowledge is acquired automatically from the extraction of verb subcategorization frames from the SenSem Corpus (Vázquez and Fernández-Montraveta, 2015) which contains the verb predicate and its arguments annotated syntactically. As a result of the information extracted, subcategorization frames have been classified into subcategorization classes regarding the patterns observed in the corpus. The results of the subcategorization classes integration in the grammars prove that this information increases the accuracy of the argument recognition in the grammars. The results of the research of this thesis show that grammars’ rules on their own are not ex- pressive enough to resolve complex ambiguities. However, the integration of knowledge about these ambiguities in the grammars may be decisive in the disambiguation. On the one hand, sta- tistical knowledge about PP-attachment can improve the grammars accuracy, but syntactic and semantic information, and new patterns of PP-attachment need to be included in the language models in order to contribute to disambiguate this phenomenon. On the other hand, linguistic knowledge about verb subcategorization acquired from annotated linguistic resources show a positive influence positively on grammars’ accuracy.Aquesta tesi vol tractar les limitacions amb què es troben els analitzadors sintàctics automàtics actualment. Tot i els progressos que s’han fet en l’àrea del Processament del Llenguatge Nat- ural en els darrers anys, les tecnologies del llenguatge i, en particular, els analitzadors sintàc- tics automàtics no han pogut traspassar el llindar de certes ambiguïtats estructurals com ara l’agrupació del sintagma preposicional i el reconeixement d’arguments. És per aquest motiu que la recerca duta a terme en aquesta tesi té com a objectiu aportar millores signiflcatives de quali- tat a l’anàlisi sintàctica automàtica per mitjà de la integració de coneixement lingüístic i estadístic per desambiguar construccions sintàctiques ambigües. El punt de partida de la recerca ha estat el desenvolupament de d’una gramàtica en espanyol i una altra en català basades en regles que segueixen els postulats de la Gramàtica de Dependèn- dencies (Tesnière, 1959; Mel’čuk, 1988) per tal de dur a terme els experiments sobre l’adquisició de coneixement automàtic. Per tal de crear dues gramàtiques robustes que analitzin i entenguin l’oració en profunditat, ens hem basat en l’arquitectura de FreeLing (Padró et al., 2010), una lli- breria de Processament de Llenguatge Natural que proveeix una anàlisi lingüística automàtica de l’oració. Per una altra banda, s’ha elaborat una proposta eclèctica de criteris lingüístics per determinar la formació dels sintagmes i les clàusules a la gramàtica per mitjà de la revisió de les propostes teòriques de la Gramàtica Generativa (Chomsky, 1981; Bonet and Solà, 1986; Haege- man, 1991) i de la Gramàtica de Dependències (Tesnière, 1959; Mel’čuk, 1988). Aquesta proposta s’acompanya d’un llistat de les etiquetes de relació de dependència que fan servir les regles de les gramàtques. A més a més de l’elaboració d’aquest llistat, s’han establert les correspondències amb l’estàndard d’anotació de les Dependències Universals (Mcdonald et al., 2013). Alhora, s’ha dissenyat un sistema d’avaluació empíric que té en compte l’anàlisi quantitativa i qualitativa per tal de fer una valoració completa dels resultats dels experiments. Precisament, es tracta una tasca empírica pel fet que es comparen les anàlisis generades per les gramàtiques amb dades reals de la llengua. Per tal de dur a terme l’avaluació des d’una perspectiva quan- titativa, s’ha fet servir el corpus Tibidabo en espanyol (Marimon et al., 2014) disponible només en espanyol que és prou extens per construir una anàlisi real de les gramàtiques i que ha estat anotat amb el mateix formalisme que les gramàtiques. En concret, per tal com els criteris de les gramàtiques i del corpus no són coincidents, s’ha dut a terme un procés d’harmonització de cri- teris per mitjà d’unes regles creades manualment que adapten automàticament l’estructura i la relació de dependència del corpus al criteri de les gramàtiques. Pel que fa a l’avaluació qualitativa, pel fet que no hi ha recursos disponibles en espanyol i català, hem dissenyat un reprertori de test de fenòmens sintàctics estructurals i relacionats amb l’ordre de l’oració. Amb l’objectiu de crear un repertori representatiu de les llengües estudiades, s’han fet servir gramàtiques descriptives per fornir el repertori d’estructures sintàctiques (Bosque and Demonte, 1999; Solà et al., 2002) i el Corpus SenSem (Vázquez and Fernández-Montraveta, 2015) per capturar automàticament l’ordre oracional. Gràcies a aquestes dues eines, s’han pogut dur a terme dos experiments per provar que la integració de coneixement en l’anàlisi sintàctica automàtica en millora la qualitat. D’una banda, Parsing and Evaluation Improving Dependency Grammars Accuracy s’ha explorat l’aprenentatge de models de llenguatge per mitjà de models estadístics per tal de proposar solucions a l’agrupació del sintagma preposicional. Més concretament, s’ha desen- volupat un model de llenguatge per mitjà d’un classiflcador d’aprenentatge supervisat de Weka (Witten and Frank, 2005). A més a més, s’ha après un model de llenguatge per mitjà d’un mètode no supervisat basat en l’aproximació distribucional anomenat word embeddings (Mikolov et al., 2013a,b). Els resultats de l’experiment posen de manifest que el mètode supervisat té greus lim- itacions per fer donar una resposta en dades que no ha vist prèviament, cosa que és superada pel mètode no supervisat pel fet que és capaç de classiflcar qualsevol cas. De tota manera, el mètode no supervisat que s’ha estudiat és limitat si aprèn a partir de dades lèxiques. Per aquesta raó, és necessari que les dades utilitzades per entrenar el model continguin el valor de la preposi- ció, trets sintàctics i semàntics. A més a més, cal ampliar el número de patrons apresos per tal d’ampliar la cobertura dels models i tenir un impacte en els resultats de les gramàtiques. D’una altra banda, s’ha proposat una manera de millorar el reconeixement d’arguments a les gramàtiques per mitjà de l’adquisició de coneixement lingüístic. En aquest experiment, s’ha op- tat per extreure automàticament el coneixement en forma de classes de subcategorització verbal d’el Corpus SenSem (Vázquez and Fernández-Montraveta, 2015), que conté anotats sintàctica- ment el predicat verbal i els seus arguments. A partir de la informació extreta, s’ha classiflcat les diverses diàtesis verbals en classes de subcategorització verbal en funció dels patrons observats en el corpus. Els resultats de la integració de les classes de subcategorització a les gramàtiques mostren que aquesta informació determina positivament el reconeixement dels arguments. Els resultats de la recerca duta a terme en aquesta tesi doctoral posen de manifest que les regles de les gramàtiques no són prou expressives per elles mateixes per resoldre ambigüitats complexes del llenguatge. No obstant això, la integració de coneixement sobre aquestes am- bigüitats pot ser decisiu a l’hora de proposar una solució. D’una banda, el coneixement estadístic sobre l’agrupació del sintagma preposicional pot millorar la qualitat de les gramàtiques, però per aflrmar-ho cal incloure informació sintàctica i semàntica en els models d’aprenentatge automàtic i capturar més patrons per contribuir en la desambiguació de fenòmens complexos. D’una al- tra banda, el coneixement lingüístic sobre subcategorització verbal adquirit de recursos lingüís- tics anotats influeix decisivament en la qualitat de les gramàtiques per a l’anàlisi sintàctica au- tomàtica

Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021

The eighth edition of the Italian Conference on Computational Linguistics (CLiC-it 2021) was held at Università degli Studi di Milano-Bicocca from 26th to 28th January 2022. After the edition of 2020, which was held in fully virtual mode due to the health emergency related to Covid-19, CLiC-it 2021 represented the first moment for the Italian research community of Computational Linguistics to meet in person after more than one year of full/partial lockdown

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Autoencoders for natural language semantics

Les auto-encodeurs sont des réseaux de neurones artificiels qui apprennent des représentations. Dans un auto-encodeur, l’encodeur transforme une entrée en une représentation, et le décodeur essaie de prédire l’entrée à partir de la représentation. Cette thèse compile trois applications de ces modèles au traitement automatique des langues : pour l’apprentissage de représentations de mots et de phrases, ainsi que pour mieux comprendre la compositionnalité. Dans le premier article, nous montrons que nous pouvons auto-encoder des définitions de dictionnaire et ainsi apprendre des vecteurs de définition. Nous proposons une nouvelle pénalité qui nous permet d’utiliser ces vecteurs comme entrées à l’encodeur lui-même, mais aussi de les mélanger des vecteurs distributionnels pré-entraînés. Ces vecteurs de définition capturent mieux la similarité sémantique que les méthodes distributionnelles telles que word2vec. De plus, l’encodeur généralise à un certain degré à des définitions qu’il n’a pas vues pendant l’entraînement. Dans le deuxième article, nous analysons les représentations apprises par les auto-encodeurs variationnels séquence-à-séquence. Nous constatons que les encodeurs ont tendance à mémo- riser les premiers mots et la longueur de la phrase d’entrée. Cela limite considérablement leur utilité en tant que modèles génératifs contrôlables. Nous analysons aussi des variantes architecturales plus simples qui ne tiennent pas compte de l’ordre des mots, ainsi que des mé- thodes basées sur le pré-entraînement. Les représentations qu’elles apprennent ont tendance à encoder plus nettement des caractéristiques globales telles que le sujet et le sentiment, et cela se voit dans les reconstructions qu’ils produisent. Dans le troisième article, nous utilisons des simulations d’émergence du langage pour étudier la compositionnalité. Un locuteur – l’encodeur – observe une entrée et produit un message. Un auditeur – le décodeur – tente de reconstituer ce dont le locuteur a parlé dans son message. Nous émettons l’hypothèse que faire des phrases impliquant plusieurs entités, telles que « Jean aime Marie », nécessite fondamentalement de percevoir chaque entité comme un tout. Nous dotons certains agents de cette capacité grâce à un mechanisme d’attention, alors que d’autres en sont privés. Nous proposons différentes métriques qui mesurent à quel point les langues des agents sont naturelles en termes de structure d’argument, et si elles sont davantage analytiques ou synthétiques. Les agents percevant les entités comme des touts échangent des messages plus naturels que les autres agents.Autoencoders are artificial neural networks that learn representations. In an autoencoder, the encoder transforms an input into a representation, and the decoder tries to recover the input from the representation. This thesis compiles three different applications of these models to natural language processing: for learning word and sentence representations, as well as to better understand compositionality. In the first paper, we show that we can autoencode dictionary definitions to learn word vectors, called definition embeddings. We propose a new penalty that allows us to use these definition embeddings as inputs to the encoder itself, but also to blend them with pretrained distributional vectors. The definition embeddings capture semantic similarity better than distributional methods such as word2vec. Moreover, the encoder somewhat generalizes to definitions unseen during training. In the second paper, we analyze the representations learned by sequence-to-sequence variational autoencoders. We find that the encoders tend to memorize the first few words and the length of the input sentence. This limits drastically their usefulness as controllable generative models. We also analyze simpler architectural variants that are agnostic to word order, as well as pretraining-based methods. The representations that they learn tend to encode global features such as topic and sentiment more markedly, and this shows in the reconstructions they produce. In the third paper, we use language emergence simulations to study compositionality. A speaker – the encoder – observes an input and produces a message about it. A listener – the decoder – tries to reconstruct what the speaker talked about in its message. We hypothesize that producing sentences involving several entities, such as “John loves Mary”, fundamentally requires to perceive each entity, John and Mary, as distinct wholes. We endow some agents with this ability via an attention mechanism, and deprive others of it. We propose various metrics to measure whether the languages are natural in terms of their argument structure, and whether the languages are more analytic or synthetic. Agents perceiving entities as distinct wholes exchange more natural messages than other agents

Delving into the uncharted territories of Word Sense Disambiguation

The automatic disambiguation of word senses, i.e. Word Sense Disambiguation, is a long-standing task in the field of Natural Language Processing; an AI-complete problem that took its first steps more than half a century ago, and which, to date, has apparently attained human-like performances on standard evaluation benchmarks. Unfortunately, the steady evolution that the task experienced over time in terms of sheer performance has not been followed hand in hand by adequate theoretical support, nor by careful error analysis. Furthermore, we believe that the lack of an exhaustive bird’s eye view which accounts for the sort of high-end and unrealistic computational architectures that systems will soon need in order to further refine their performances could lead the field to a dead angle in a few years. In essence, taking advantage of the current moment of great accomplishments and renewed interest in the task, we argue that Word Sense Disambiguation is mature enough for researchers to really observe the extent of the results hitherto obtained, evaluate what is actually missing, and answer the much sought for question: “are current state-of-the-art systems really able to effectively solve lexical ambiguity?” Driven by the desire to become both architects and participants in this period of pondering, we have identified a few macro-areas representatives of the challenges of automatic disambiguation. From this point of view, in this thesis, we propose experimental solutions and empirical tools so as to bring to the attention of the Word Sense Disambiguation community unusual and unexplored points of view. We hope these will represent a new perspective through which to best observe the current state of disambiguation, as well as to foresee future paths for the task to evolve on. Specifically, 1q) prompted by the growing concern about the rise in performance being closely linked to the demand for more and more unrealistic computational architectures in all areas of application of Deep Learning related techniques, we 1a) provide evidence for the undisclosed potential of approaches based on knowledge-bases, via the exploitation of syntagmatic information. Moreover, 2q) driven by the dissatisfaction with the use of cognitively-inaccurate, finite inventories of word senses in Word Sense Disambiguation, we 2a) introduce an approach based on Definition Modeling paradigms to generate contextual definitions for target words and phrases, hence going beyond the limits set by specific lexical-semantic inventories. Finally, 3q) moved by the desire to analyze the real implications beyond the idea of “machines performing disambiguation on par with their human counterparts” we 3a) put forward a detailed analysis of the shared errors affecting current state-of-the-art systems based on diverse approaches for Word Sense Disambiguation, and highlight, by means of a novel evaluation dataset tailored to represent common and critical issues shared by all systems, performances way lower than those usually reported in the current literature

Event knowledge in large language models: the gap between the impossible and the unlikely

Word co-occurrence patterns in language corpora contain a surprising amount of conceptual knowledge. Large language models (LLMs), trained to predict words in context, leverage these patterns to achieve impressive performance on diverse semantic tasks requiring world knowledge. An important but understudied question about LLMs' semantic abilities is whether they acquire generalized knowledge of common events. Here, we test whether five pre-trained LLMs (from 2018's BERT to 2023's MPT) assign higher likelihood to plausible descriptions of agent-patient interactions than to minimally different implausible versions of the same event. Using three curated sets of minimal sentence pairs (total n=1,215), we found that pre-trained LLMs possess substantial event knowledge, outperforming other distributional language models. In particular, they almost always assign higher likelihood to possible vs. impossible events (The teacher bought the laptop vs. The laptop bought the teacher). However, LLMs show less consistent preferences for likely vs. unlikely events (The nanny tutored the boy vs. The boy tutored the nanny). In follow-up analyses, we show that (i) LLM scores are driven by both plausibility and surface-level sentence features, (ii) LLM scores generalize well across syntactic variants (active vs. passive constructions) but less well across semantic variants (synonymous sentences), (iii) some LLM errors mirror human judgment ambiguity, and (iv) sentence plausibility serves as an organizing dimension in internal LLM representations. Overall, our results show that important aspects of event knowledge naturally emerge from distributional linguistic patterns, but also highlight a gap between representations of possible/impossible and likely/unlikely events.Comment: The two lead authors have contributed equally to this wor