AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes

Rothe, Sascha; Schütze, Hinrich

doi:10.3115/v1/p15-1173

Cited by 207 publications

(216 citation statements)

References 22 publications

(15 reference statements)

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“…Related Work Canonicalization, or WSD (Pal and Saha 2015), has been used in numerous applications, including machine translation, information retrieval, and information extraction (Rothe and Schütze 2015;Leacock et al 1998). In English sentences, sentences like "He scored a goal" and "It was his goal in life" carry different meanings for the word "goal."…”

Section: Canonicalization Statisticsmentioning

confidence: 99%

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

Krishna

Zhu

Groth³

et al. 2017

Int J Comput Vis

3,899

3,078

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Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. When asked "What vehicle is the person riding?", computers will need to identify the objects in an image as well as the relationships riding(man, carriage) and pulling(horse, carriage) to answer correctly that "the person is riding a horse-drawn carriage." In this paper, we present the Visual Genome dataset to enable the modeling of such relationships. We collect dense annotations of objects, attributes, and relationships within each image to learn these models. Specifically, our dataset contains over 108K images where each image has an average of 35 objects, 26 attributes, and 21 pairwise relationships between objects. We canonicalize the objects, attributes, relationships, and noun phrases in region descriptions and questions answer pairs to WordNet synsets. Together, these annotations represent the densest

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Section: Canonicalization Statisticsmentioning

confidence: 99%

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

Krishna

Zhu

Groth³

et al. 2017

Int J Comput Vis

3,899

3,078

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“…S(w) denotes the set of all the senses of word w. We assume that the sets of senses of different words do not overlap. Therefore, in this paper a word sense can be seen as a lexeme of the word (Rothe and Schutze, 2015). Our model can be represented as a Markov network shown in Figure 1.…”

Section: Modelmentioning

confidence: 99%

Context-Dependent Sense Embedding

Qiu

2016

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Word embedding has been widely studied and proven helpful in solving many natural language processing tasks. However, the ambiguity of natural language is always a problem on learning high quality word embeddings. A possible solution is sense embedding which trains embedding for each sense of words instead of each word. Some recent work on sense embedding uses context clustering methods to determine the senses of words, which is heuristic in nature. Other work creates a probabilistic model and performs word sense disambiguation and sense embedding iteratively. However, most of the previous work has the problems of learning sense embeddings based on imperfect word embeddings as well as ignoring the dependency between sense choices of neighboring words. In this paper, we propose a novel probabilistic model for sense embedding that is not based on problematic word embedding of polysemous words and takes into account the dependency between sense choices. Based on our model, we derive a dynamic programming inference algorithm and an ExpectationMaximization style unsupervised learning algorithm. The empirical studies show that our model outperforms the state-of-the-art model on a word sense induction task by a 13% relative gain.

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“…Previously, other approaches were introduced to utilise embeddings for supervised (Zhong and Ng, 2010;Rothe and Schütze, 2015; Taghipour and Ng, 2015) and knowledge-based WSD (Chen et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the Third Workshop on Discourse in Machine Translation

2017

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We hope that workshops such as this one will continue to stimulate work on Discourse and Machine Translation, in a wide range of discourse phenomena and MT architectures.We would like to thank all the authors who submitted papers to the workshop, as well as all the members of the Program Committee who reviewed the submissions and delivered thoughtful, informative reviews. AbstractWe describe the design, the setup, and the evaluation results of the DiscoMT 2017 shared task on cross-lingual pronoun prediction. The task asked participants to predict a target-language pronoun given a source-language pronoun in the context of a sentence. We further provided a lemmatized target-language human-authored translation of the source sentence, and automatic word alignments between the source sentence words and the targetlanguage lemmata. The aim of the task was to predict, for each target-language pronoun placeholder, the word that should replace it from a small, closed set of classes, using any type of information that can be extracted from the entire document.We offered four subtasks, each for a different language pair and translation direction: English-to-French, Englishto-German, German-to-English, and Spanish-to-English.Five teams participated in the shared task, making submissions for all language pairs. The evaluation results show that all participating teams outperformed two strong n-gram-based language model-based baseline systems by a sizable margin. IntroductionPronoun translation poses a problem for machine translation (MT) as pronoun systems do not map well across languages, e.g., due to differences in gender, number, case, formality, or humanness, as well as because of language-specific restrictions about where pronouns may be used. For example, when translating the English it into French an MT system needs to choose between il, elle, and cela, while translating the same pronoun into German would require a choice between er, sie, and es. This is hard as selecting the correct pronoun may need discourse analysis as well as linguistic and world knowledge. Null subjects in pro-drop languages pose additional challenges as they express person and number within the verb's morphology, rendering a subject pronoun or noun phrase redundant. Thus, translating from such languages requires generating a pronoun in the target language for which there is no pronoun in the source.Pronoun translation is known to be challenging not only for MT in general, but also for Statistical Machine Translation (SMT) in particular (Le Nagard and Koehn, 2010;Hardmeier and Federico, 2010; Novák, 2011;. Phrase-based SMT (Koehn et al., 2013) was state of the art until recently, but it is gradually being replaced by Neural Machine Translation, or NMT, (Cho et al., 2014; Sutskever et al., 2014; Bahdanau et al., 2015;Luong et al., 2015). 1 NMT yields generally higher-quality translation, but is harder to analyze, and thus little is known about how well it handles pronoun translation. Yet, it is clear that it has access to larger context compa...

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AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes

Cited by 207 publications

References 22 publications

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

Context-Dependent Sense Embedding

Proceedings of the Third Workshop on Discourse in Machine Translation

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