4,616 research outputs found
Obtaining referential word meanings from visual and distributional information: Experiments on object naming
Zarrieß S, Schlangen D. Obtaining referential word meanings from visual and distributional information: Experiments on object naming. In: Proceedings of 55th annual meeting of the Association for Computational Linguistics (ACL). Vancouver; 2017
Easy Things First: Installments Improve Referring Expression Generation for Objects in Photographs
Zarrieß S, Schlangen D. Easy Things First: Installments Improve Referring Expression Generation for Objects in Photographs. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016). 2016
Resolving References to Objects in Photographs using the Words-As-Classifiers Model
Schlangen D, Zarrieß S, Kennington C. Resolving References to Objects in Photographs using the Words-As-Classifiers Model. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016). Berlin; 2016
Learning to Parse and Translate Improves Neural Machine Translation
There has been relatively little attention to incorporating linguistic prior
to neural machine translation. Much of the previous work was further
constrained to considering linguistic prior on the source side. In this paper,
we propose a hybrid model, called NMT+RNNG, that learns to parse and translate
by combining the recurrent neural network grammar into the attention-based
neural machine translation. Our approach encourages the neural machine
translation model to incorporate linguistic prior during training, and lets it
translate on its own afterward. Extensive experiments with four language pairs
show the effectiveness of the proposed NMT+RNNG.Comment: Accepted as a short paper at the 55th Annual Meeting of the
Association for Computational Linguistics (ACL 2017
Analysing Lexical Semantic Change with Contextualised Word Representations
This paper presents the first unsupervised approach to lexical semantic
change that makes use of contextualised word representations. We propose a
novel method that exploits the BERT neural language model to obtain
representations of word usages, clusters these representations into usage
types, and measures change along time with three proposed metrics. We create a
new evaluation dataset and show that the model representations and the detected
semantic shifts are positively correlated with human judgements. Our extensive
qualitative analysis demonstrates that our method captures a variety of
synchronic and diachronic linguistic phenomena. We expect our work to inspire
further research in this direction.Comment: To appear in Proceedings of the 58th Annual Meeting of the
Association for Computational Linguistics (ACL-2020
Unsupervised Learning of Style-sensitive Word Vectors
This paper presents the first study aimed at capturing stylistic similarity
between words in an unsupervised manner. We propose extending the continuous
bag of words (CBOW) model (Mikolov et al., 2013) to learn style-sensitive word
vectors using a wider context window under the assumption that the style of all
the words in an utterance is consistent. In addition, we introduce a novel task
to predict lexical stylistic similarity and to create a benchmark dataset for
this task. Our experiment with this dataset supports our assumption and
demonstrates that the proposed extensions contribute to the acquisition of
style-sensitive word embeddings.Comment: 7 pages, Accepted at The 56th Annual Meeting of the Association for
Computational Linguistics (ACL 2018
Topically Driven Neural Language Model
Language models are typically applied at the sentence level, without access
to the broader document context. We present a neural language model that
incorporates document context in the form of a topic model-like architecture,
thus providing a succinct representation of the broader document context
outside of the current sentence. Experiments over a range of datasets
demonstrate that our model outperforms a pure sentence-based model in terms of
language model perplexity, and leads to topics that are potentially more
coherent than those produced by a standard LDA topic model. Our model also has
the ability to generate related sentences for a topic, providing another way to
interpret topics.Comment: 11 pages, Proceedings of the 55th Annual Meeting of the Association
for Computational Linguistics (ACL 2017) (to appear
Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation
Rating scales are a widely used method for data annotation; however, they
present several challenges, such as difficulty in maintaining inter- and
intra-annotator consistency. Best-worst scaling (BWS) is an alternative method
of annotation that is claimed to produce high-quality annotations while keeping
the required number of annotations similar to that of rating scales. However,
the veracity of this claim has never been systematically established. Here for
the first time, we set up an experiment that directly compares the rating scale
method with BWS. We show that with the same total number of annotations, BWS
produces significantly more reliable results than the rating scale.Comment: In Proceedings of the Annual Meeting of the Association for
Computational Linguistics (ACL), Vancouver, Canada, 201
- …