Aspectuality Across Genre: A Distributional Semantics Approach (2020)

Abstract: The interpretation of the lexical aspect of verbs in English plays a crucial role in tasks such as recognizing textual entailment and learning discourse-level inferences. We show that two elementary dimensions of aspectual class, states vs. events, and telic vs. atelic events, can be modelled effectively with distributional semantics. We find that a verb’s local context is most indicative of its aspectual class, and we demonstrate that closed class words tend to be stronger discriminating contexts than content words. Our approach outperforms previous work on three datasets. Further, we present a new dataset of human-human conversations annotated with lexical aspects and present experiments that show the correlation of telicity with genre and discourse goals.

## introduction one of the fascinating aspects of studying aspectual class of verbs in english is its relation with nonverbal categories. thus, although in origin a property of the verb, the aspectual class interacts in a tight-knit fashion with other words in a sentence. previous research has discussed the importance of predicting the aspectual classes of verbs for predicting coherence relations in text and imagery @xcite , predicting links in entailment graphs @xcite and interpreting sign languages @xcite . in addition, knowledge about the aspectual class of a verb phrase, and its influence on the temporal extent and entailments that it licenses, has been leveraged in the past for a number of natural language understanding tasks such as temporal relation extraction @xcite , event ordering @xcite , and statistical machine translation @xcite . the aktionsart @xcite of a verb determines the temporal extent of the predication as well as whether it causes a change of state for the entities involved @xcite . as aktionsart typically refers to the lexical aspect of a verb in isolation, we adopt the terminology of @xcite , and refer to the compositionally formed aktionsart of a verb phrase as predicational aspect. one of the most important distinctions of the predicational aspect of a verb is between states, such as to know or to love, and events, such as visit or swim. this distinction is important for identifying the entailments that a given verb phrase licenses, as stative predications do not, by definition, entail any change of state. this property has important consequences for a number of natural language understanding tasks such as question answering. for example, if it is known that john has arrived in vienna, a system leveraging aspectual information will be able to infer that the completion of the event of arriving in vienna, indicated by the perfect vp having arrived in, has caused a change of state which entails being in. therefore, when asked where is john?, the system will be able to produce the correct answer: vienna. on the other hand, a predominantly stative verb such as to know, as in eve knows a lot about quantum mechanics, does not cause a change of state for either eve or quantum mechanics. telic predicates do not license consequent state inferences from their progressive vp forms to corresponding non-progressive forms. 1 thus, telic/atelic classifications are supported by contrastive pairs like the following: ## related work an early approach to classifying the lexical aspectual class of a verb in context was proposed by @xcite , who applied a decompositional analysis of the verb to determine the aspectual class for verb occurrences in a restricted domain. the first general-purpose study was conducted by @xcite , who built up on earlier work by @xcite , and collected linguistic indicators for lexical aspect from a large corpus. these include the presence of inor for-adverbials, the tense of the verb or its frequency. @xcite subsequently applied different supervised machine learning algorithms to classify the extracted feature vectors into either states or events, or telic or atelic events. @xcite show that their method substantially improves over a majority-class baseline. the first approach to include features derived from a distributional semantic model has been proposed by @xcite . in addition to the linguistic indicator features of @xcite , @xcite extract representative stative, dynamic or mixed verbs from the lexical conceptual structure (lcs) database @xcite and subsequently use distributional representations to derive similarity scores for the mined verbs. another extension to the work of @xcite has been proposed by @xcite , who refines the distributional similarity features by first contextualising a target verb with its subject or object, and only then computing the distributional similarities to the set of representative verbs from the lcs database as in @xcite . all else being equal, @xcite shows that contextualising the distributional representations improves performance on the asp-ambig dataset of @xcite . in contrast to this line of research we do not make explicit use of any hand-engineered linguistic indicator features but show that these can be picked up in an unsupervised way by composing distributional semantic word representations. the linguistic indicators are furthermore frequently collected on the verb type level instead of on the token level. similar to @xcite , we are concerned with classifying verb readings; however, we do not use engineered features as @xcite do, but directly leverage local contextual information in the form of distributional representations. our approach is also not reliant on the availability of a parallel corpus as in @xcite . the major difference between our approach of using distributional word representations and previous approaches is that we are using the word representations directly for classification, rather than indirectly by computing similarity scores and using these as features. this furthermore liberates us from the requirement of having a representative seed set of verbs per class to compute the distributional similarities from. ## dataset we introduce a new dataset, diaspora, 2 of human-human conversations annotated with predicational aspect, representing the first dialogue dataset annotated with aspectual information. as reviewed in section 2, what partly makes computational research on predicational aspect difficult is the lack of diverse annotated corpora. with the release of diaspora we provide a situated conversational perspective on predicational aspect research, and thereby extend the existing evaluation repertoire by a very important genre. annotation effort. as our starting point, we sampled 2000 utterances from the walking around corpus @xcite uniformly at random. the walking around corpus is a dataset of humanhuman phone conversations where one party needs to find certain landmarks on a university campus and receives directions via phone from the other party. we chose the walking around corpus because its conversations are situated and in real-time, and because it contains a good distribution of stative, telic, and atelic verb phrases. after sampling the initial set of 2000 utterances, we filtered multi-sentence utterances and utterances that did not contain a verb. we furthermore removed any filled pauses (indicated by "(..)" in the study has been approved by rutgers's irb. expert annotators annotated the whole dataset and were paid an hourly rate of 15 usd. they were final year linguistics undergraduate students and were provided with an annotation protocol for their task. 3 to assess the inter-annotator agreement, we determine cohen's κ value. we randomly selected 200 sentences and assigned each to two annotators, obtaining a cohen's κ of 0.81, which indicates almost perfect agreement @xcite . overall statistics. the final diaspora dataset contains 927 annotated utterances, consisting of 400 utterances labelled as expressing stative predicational aspect (43%), 279 labelled as telic (30%), and 248 labelled as atelic (27%). the overall average utterance length is 15.58. for diaspora per individual label. the means and medians are relatively similar across all classes, suggesting that there is no bias in terms of utterance lengths for any individual class. the diaspora dataset contains 98 unique verb forms, spanning 69 lemmas, with the top 10 most frequent verb lemmas making up ≈78% of all verbs in the corpus. the characteristic of few verbs making up a large proportion of the overall data has already been observed for captions @xcite . this is an expected property in diaspora and is due to the single-domain nature of the walking around corpus. figure 1 shows the frequency distribution of the top 10 most frequent verbs and their associated label distribution. the large proportion of various forms of be is due to many utterances of either speaker referring to the current location of the subject looking for the landmark (e.g. utterances like i am currently at . . . ). the label distribution of the 10 most frequent verbs shows that there are some highly skewed verbs, such as get, see or know, which have a clear majority class, whereas do or look exhibit a much more balanced, and therefore ambiguous label distribution. ## experiments the utility of distributional semantic word representations has been shown in a large body of works in recent years @xcite , @xcite ; passim). in order to compose a verb with its context we apply pointwise addition as a simple distributional composition function. pointwise addition in neural word embeddings approximates the intersection of their contexts foot_3 @xcite , and has been shown to be an efficient function for contextualising a word in a phrase @xcite . ## experiments we perform experiments that assess the suitability of distributional representations for distinguishing states from events ( § 5.1), and telic from atelic events ( § 5.2). only a completed and telic event licenses a new consequent state. therefore, modelling predicational aspect is important for deeper text understanding, for example for modelling cause and effect, and especially for inferring consequent states. ## conclusion in this work, we have proposed the first dataset of human-human dialogues annotated with the aspectual class of verbs. we have proposed a compositional distributional approach for modelling the aspectual class of english verbs in context. our results indicate that distributional models are able to learn concise representations for closed class words such as particles and prepositions, and that classifiers using composed distributional representations achieve a new state-of-the-art on three recently proposed datasets. we have furthermore contributed a qualitative analysis, providing empirical evidence for the long standing insight of semanticists that the presence of prepositions or particles in a verb phrase, tend to be very reliable indicators of the verb's aspectual class @xcite , @xcite , @xcite , passim). our model setup was intentionally kept simple as we were primarily concerned with the question whether predicational aspect can be captured with a distributional semantics approach in principle. we note that using more sophisticated models might yield even stronger results, although in preliminary tests, we did not observe any meaningful performance difference when replacing our bagof-embeddings approach with elmo @xcite or bert @xcite . while this work was done on english, we aim to use our methodology in a multilingual setup in future work as distributional approaches scale well with growing amounts of data and across languages. aspect, alongside tense, is a crucial indicator of the temporal extent of a verb as well as the entailments it licenses. in future work we plan to integrate aspectual information for improving the unsupervised construction of entailment graphs @xcite , as well as temporal reasoning, which has been shown recently to be difficult for distributional semantic models @xcite . aspectual information can be utilised for directional entailment detection by inferring that the event of buying something entails the state of owning that thing, but not the other way round. determining the telicity of an event also enables fine-grained inferences about whether an event caused a change of state. for example, while the telic context of writing a sonnet in fifteen minutes entails a change to a state where a finished sonnet exists, the atelic context of writing a sonnet for fifteen minutes does not.