18 research outputs found
Story Cloze Ending Selection Baselines and Data Examination
This paper describes two supervised baseline systems for the Story Cloze Test
Shared Task (Mostafazadeh et al., 2016a). We first build a classifier using
features based on word embeddings and semantic similarity computation. We
further implement a neural LSTM system with different encoding strategies that
try to model the relation between the story and the provided endings. Our
experiments show that a model using representation features based on average
word embedding vectors over the given story words and the candidate ending
sentences words, joint with similarity features between the story and candidate
ending representations performed better than the neural models. Our best model
achieves an accuracy of 72.42, ranking 3rd in the official evaluation.Comment: Submission for the LSDSem 2017 - Linking Models of Lexical,
Sentential and Discourse-level Semantics - Shared Tas
Comparative Analysis of Word Embeddings for Capturing Word Similarities
Distributed language representation has become the most widely used technique
for language representation in various natural language processing tasks. Most
of the natural language processing models that are based on deep learning
techniques use already pre-trained distributed word representations, commonly
called word embeddings. Determining the most qualitative word embeddings is of
crucial importance for such models. However, selecting the appropriate word
embeddings is a perplexing task since the projected embedding space is not
intuitive to humans. In this paper, we explore different approaches for
creating distributed word representations. We perform an intrinsic evaluation
of several state-of-the-art word embedding methods. Their performance on
capturing word similarities is analysed with existing benchmark datasets for
word pairs similarities. The research in this paper conducts a correlation
analysis between ground truth word similarities and similarities obtained by
different word embedding methods.Comment: Part of the 6th International Conference on Natural Language
Processing (NATP 2020
Towards Efficient Scoring of Student-generated Long-form Analogies in STEM
Switching from an analogy pedagogy based on comprehension to analogy pedagogy based on production raises an impractical manual analogy scoring problem. Conventional symbol-matching approaches to computational analogy evaluation focus on positive cases, and challenge computational feasibility. This work presents the Discriminative Analogy Features (DAF) pipeline to identify the discriminative features of strong and weak long-form text analogies. We introduce four feature categories (semantic, syntactic, sentiment, and statistical) used with supervised vector-based learning methods to discriminate between strong and weak analogies. Using a modestly sized vector of engineered features with SVM attains a 0.67 macro F1 score. While a semantic feature is the most discriminative, out of the top 15 discriminative features, most are syntactic. Combining these engineered features with an ELMo-generated embedding still improves classification relative to an embedding alone. While an unsupervised K-Means clustering-based approach falls short, similar hints of improvement appear when inputs include the engineered features used in supervised learning
Like a bilingual baby: The advantage of visually grounding a bilingual language model
Unlike most neural language models, humans learn language in a rich,
multi-sensory and, often, multi-lingual environment. Current language models
typically fail to fully capture the complexities of multilingual language use.
We train an LSTM language model on images and captions in English and Spanish
from MS-COCO-ES. We find that the visual grounding improves the model's
understanding of semantic similarity both within and across languages and
improves perplexity. However, we find no significant advantage of visual
grounding for abstract words. Our results provide additional evidence of the
advantages of visually grounded language models and point to the need for more
naturalistic language data from multilingual speakers and multilingual datasets
with perceptual grounding.Comment: Preprint, 7 pages, 2 tables, 1 figur
Synonym Detection Using Syntactic Dependency And Neural Embeddings
Recent advances on the Vector Space Model have significantly improved some
NLP applications such as neural machine translation and natural language
generation. Although word co-occurrences in context have been widely used in
counting-/predicting-based distributional models, the role of syntactic
dependencies in deriving distributional semantics has not yet been thoroughly
investigated. By comparing various Vector Space Models in detecting synonyms in
TOEFL, we systematically study the salience of syntactic dependencies in
accounting for distributional similarity. We separate syntactic dependencies
into different groups according to their various grammatical roles and then use
context-counting to construct their corresponding raw and SVD-compressed
matrices. Moreover, using the same training hyperparameters and corpora, we
study typical neural embeddings in the evaluation. We further study the
effectiveness of injecting human-compiled semantic knowledge into neural
embeddings on computing distributional similarity. Our results show that the
syntactically conditioned contexts can interpret lexical semantics better than
the unconditioned ones, whereas retrofitting neural embeddings with semantic
knowledge can significantly improve synonym detection