Animacy is a necessary property for a referent to be an agent, and thus animacy detection is useful for a variety of natural language processing tasks, including word sense disambiguation, co-reference resolution, semantic role labeling, and others. Prior work treated animacy as a word-level property, and has developed statistical classifiers to classify words as either animate or inanimate. We discuss why this approach to the problem is ill-posed, and present a new approach based on classifying the animacy of co-reference chains. We show that simple voting approaches to inferring the animacy of a chain from its constituent words perform relatively poorly, and then present a hybrid system merging supervised machine learning (ML) and a small number of hand-built rules to compute the animacy of referring expressions and co-reference chains. This method achieves state of the art performance. The supervised ML component leverages features such as word embeddings over referring expressions, parts of speech, and grammatical and semantic roles. The rules take into consideration parts of speech and the hypernymy structure encoded in WordNet. The system achieves an F1 of 0.88 for classifying the animacy of referring expressions, which is comparable to state of the art results for classifying the animacy of words, and achieves an F1 of 0.75 for classifying the animacy of coreference chains themselves. We release our training and test dataset, which includes 142 texts (all narratives) comprising 156,154 words, 34,698 referring expressions, and 10,941 co-reference chains. We test the method on a subset of the OntoNotes dataset, showing using manual sampling that animacy classification is 90% +/- 2% accurate for coreference chains, and 92% +/- 1% for referring expressions. The data also contains 46 folktales, which present an interesting challenge because they often involve characters who are members of traditionally inanimate classes (e.g., stoves that walk, trees that talk). We show that our system is able to detect the animacy of these unusual referents with an F1 of 0.95.
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Confirming the Generalizability of a Chain-Based Animacy Detector
Animacy is the characteristic of a referent beingable to independently carry out actions in a storyworld (e.g., movement, communication). It is anecessary property of characters in stories, and sodetecting animacy is an important step in automaticstory understanding; it is also potentially useful formany other natural language processing tasks suchas word sense disambiguation, coreference resolu-tion, character identification, and semantic role la-beling. Recent work by Jahanet al.[2018]demon-strated a new approach to detecting animacy whereanimacy is considered a direct property of corefer-ence chains (and referring expressions) rather thanwords. In Jahanet al., they combined hand-builtrules and machine learning (ML) to identify the an-imacy of referring expressions and used majorityvoting to assign the animacy of coreference chains,and reported high performance of up to 0.90F1. Inthis short report we verify that the approach gener-alizes to two different corpora (OntoNotes and theCorpus of English Novels) and we confirmed thatthe hybrid model performs best, with the rule-basedmodel in second place. Our tests apply the animacyclassifier to almost twice as much data as Jahanetal.’s initial study. Our results also strongly suggest,as would be expected, the dependence of the mod-els on coreference chain quality. We release ourdata and code to enable reproducibility.
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- Award ID(s):
- 1749917
- NSF-PAR ID:
- 10220129
- Date Published:
- Journal Name:
- 1st Workshop on Artificial Intelligence for Narratives (AI4N 2020)
- Page Range / eLocation ID:
- 43 to 46
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Referring expression comprehension aims to localize objects identified by natural language descriptions. This is a challenging task as it requires understanding of both visual and language domains. One nature is that each object can be described by synonymous sentences with paraphrases, and such varieties in languages have critical impact on learning a comprehension model. While prior work usually treats each sentence and attends it to an object separately, we focus on learning a referring expression comprehension model that considers the property in synonymous sentences. To this end, we develop an end-to-end trainable framework to learn contrastive features on the image and object instance levels, where features extracted from synonymous sentences to describe the same object should be closer to each other after mapping to the visual domain. We conduct extensive experiments to evaluate the proposed algorithm on several benchmark datasets, and demonstrate that our method performs favorably against the state-of-the-art approaches. Furthermore, since the varieties in expressions become larger across datasets when they describe objects in different ways, we present the cross-dataset and transfer learning settings to validate the ability of our learned transferable features.
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Incorporating symbolic reasoning into machine learning algorithms is a promising approach to improve performance on learning tasks that re- quire logical reasoning. We study the problem of generating a programmatic variant of referring expressions that we call referring relational pro- grams. In particular, given a symbolic representation of an image and a target object in that image, the goal is to generate a relational program that uniquely identifies the target object in terms of its attributes and its relations to other objects in the image. We propose a neurosymbolic program synthesis algorithm that combines a policy neural network with enumerative search to generate such relational programs. The policy neural net- work employs a program interpreter that provides immediate feedback on the consequences of the decisions made by the policy, and also takes into account the uncertainty in the symbolic representation of the image. We evaluate our algorithm on challenging benchmarks based on the CLEVR dataset, and demonstrate that our approach significantly outperforms several baselines.more » « less
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Referring expressions are natural language construc- tions used to identify particular objects within a scene. In this paper, we propose a unified framework for the tasks of referring expression comprehension and generation. Our model is composed of three modules: speaker, listener, and reinforcer. The speaker generates referring expressions, the listener comprehends referring expressions, and the rein- forcer introduces a reward function to guide sampling of more discriminative expressions. The listener-speaker mod- ules are trained jointly in an end-to-end learning frame- work, allowing the modules to be aware of one another during learning while also benefiting from the discrimina- tive reinforcer’s feedback. We demonstrate that this unified framework and training achieves state-of-the-art results for both comprehension and generation on three referring ex- pression datasets.more » « less
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We examine the role of referential properties and lexical stipulation in three closely related languages of eastern Indonesia, the Alor-Pantar languages Abui, Kamang, and Teiwa. Our focus is on the continuum along which event properties (e.g. volitionality, affectedness) are highly important at one extreme or play virtually no role at the other. These languages occupy different points along this continuum. In Abui, event semantics play the greatest role, while in Teiwa they play the smallest role (the lexical property animacy being dominant in the formation of verb classes). Kamang occupies an intermediate position. Teiwa has conventionalised the relation between a verb and its class along the lines of animacy so that classes become associated with the animacy value of the objects with which the verbs in a given class typically occur. Paying attention to a lexical property like animacy, in contrast with event properties, has meant greater potential for arbitrary classes to emerge.more » « less
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