Over the past decade, the field of naturallanguageprocessing has developed a wide array of computational methods for reasoning about narrative, including summarization, commonsense inference, and event detection. Wh...
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ISBN:
(纸本)9781955917094
Over the past decade, the field of naturallanguageprocessing has developed a wide array of computational methods for reasoning about narrative, including summarization, commonsense inference, and event detection. While this work has brought an important empirical lens for examining narrative, it is by and large divorced from the large body of theoretical work on narrative within the humanities, social and cognitive sciences. In this position paper, we introduce the dominant theoretical frameworks to the NLP community, situate current research in NLP within distinct narratological traditions, and argue that linking computational work in NLP to theory opens up a range of new empirical questions that would both help advance our understanding of narrative and open up new practical applications.
Morphology and syntax interact considerably in many languages and languageprocessing should pay attention to these interdependencies. We analyze the effect of syntactic features when used in automatic morphology pred...
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This paper focuses on paraphrase generation, which is a widely studied naturallanguage generation task in NLP. With the development of neural models, paraphrase generation research has exhibited a gradual shift to ne...
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ISBN:
(纸本)9781955917094
This paper focuses on paraphrase generation, which is a widely studied naturallanguage generation task in NLP. With the development of neural models, paraphrase generation research has exhibited a gradual shift to neural methods in the recent years. This has provided architectures for contextualized representation of an input text and generating fluent, diverse and human-like paraphrases. This paper surveys various approaches to paraphrase generation with a main focus on neural methods.
In this paper, we provide empirical evidence based on a rigourously studied mathematical model for bi-populated networks, that a glass ceiling within the field of NLP has developed since the mid 2000s.
ISBN:
(纸本)9781948087841
In this paper, we provide empirical evidence based on a rigourously studied mathematical model for bi-populated networks, that a glass ceiling within the field of NLP has developed since the mid 2000s.
Text annotation tools assume that their user's goal is to create a labeled corpus. However, users view annotation as a necessary evil on the way to deliver business value through NLP. Thus an annotation tool shoul...
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ISBN:
(纸本)9781955917117
Text annotation tools assume that their user's goal is to create a labeled corpus. However, users view annotation as a necessary evil on the way to deliver business value through NLP. Thus an annotation tool should optimize for the throughput of the global NLP process, not only the productivity of individual annotators. LightTag is a text annotation tool designed and built on that principle. This paper shares our design rationale, data modeling choices, and user interface decisions then illustrates how those choices serve the full NLP lifecycle.
Training higher-order conditional random fields is prohibitive for huge tag sets. We present an approximated conditional random field using coarse-to-fine decoding and early updating. We show that our implementation y...
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Due to the black-box nature of deep learning models, methods for explaining the models' results are crucial to gain trust from humans and support collaboration between AIs and humans. In this paper, we consider se...
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ISBN:
(纸本)9781950737901
Due to the black-box nature of deep learning models, methods for explaining the models' results are crucial to gain trust from humans and support collaboration between AIs and humans. In this paper, we consider several model-agnostic and model-specific explanation methods for CNNs for text classification and conduct three human-grounded evaluations, focusing on different purposes of explanations: (1) revealing model behavior, (2) justifying model predictions, and (3) helping humans investigate uncertain predictions. The results highlight dissimilar qualities of the various explanation methods we consider and show the degree to which these methods could serve for each purpose.
In this paper we explore the functionalities of ET, a suite designed to support linguistic research and naturallanguageprocessing tasks using corpora annotated in the CoNLL-U format. These goals are achieved by two ...
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ISBN:
(纸本)9781955917117
In this paper we explore the functionalities of ET, a suite designed to support linguistic research and naturallanguageprocessing tasks using corpora annotated in the CoNLL-U format. These goals are achieved by two integrated environments - Interrogatorio, an environment for querying and editing annotated corpora, and Julgamento, an environment for assessing their quality. ET is open-source, built on different Python Web technologies and has Web demonstrations available on-line. ET has been intensively used in our research group for over two years, being the chosen framework for several linguistic and NLP-related studies conducted by its researchers.
Multilingual speakers switch between languages in online and spoken communication. Analyses of large scale multilingual data require automatic language identification at the word level. For our experiments with multil...
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An anagram is a sentence or a phrase that is made by permutating the characters of an input sentence or a phrase. For example, "Trims cash" is an anagram of "Christmas". Existing automatic anagram ...
ISBN:
(纸本)9781950737901
An anagram is a sentence or a phrase that is made by permutating the characters of an input sentence or a phrase. For example, "Trims cash" is an anagram of "Christmas". Existing automatic anagram generation methods can find possible combinations of words form an anagram. However, they do not pay much attention to the naturalness of the generated anagrams. In this paper, we show that simple depth-first search can yield natural anagrams when it is combined with modern neural language models. Human evaluation results show that the proposed method can generate significantly more natural anagrams than baseline methods.
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