We present two NLP components for the Story Cloze Task – dictionary-based sentiment analysis and lexical cohesion. While previous research found no contribution from sentiment analysis to the accuracy on this task, we demonstrate that sentiment is an important aspect. We describe a new approach, using a rule that estimates sentiment congruence in a story. Our sentiment-based system achieves str...

Scripts represent knowledge of stereotypical event sequences that can aid text understanding. Initial statistical methods have been developed to learn probabilistic scripts from raw text corpora; however, they utilize a very impoverished representation of events, consisting of a verb and one dependent argument. We present a script learning approach that employs events with multiple arguments. U...

This paper reports on an investigation of lexical selfrepair in Dutch spontaneous dialogue. Lexical selfrepairs, in which one word is rejected for another, can be produced with or without notable ̳prosodic marking‘ of the second word. It remains unclear what motivates speakers‘ choices, but previous research has shown that the semantic distance between the two words is relevant. This study asse...

Implicit arguments are not syntactically connected to their predicates, and are therefore hard to extract. Previous work has used models with large numbers of features, evaluated on very small datasets. We propose to train models for implicit argument prediction on a simple cloze task, for which data can be generated automatically at scale. This allows us to use a neural model, which draws on n...

Two large-scale cloze-style context-question-answer datasets have been introduced recently: i) the CNN and Daily Mail news data and ii) the Children’s Book Test. Thanks to the size of these datasets, the associated task is well suited for deep-learning techniques that seem to outperform all alternative approaches. We present a new, simple model that is tailor made for such question-answering pr...

We present the task of answering cloze questions automatically and how it can be tackled by exploiting lexical knowledge bases (LKBs). This task was performed in what can be seen as an indirect evaluation of Portuguese LKB. We introduce the LKBs used and the algorithms applied, and then report on the obtained results and draw some conclusions: LKBs are definitely useful resources for this chall...

In 2005, DeLong, Urbach, and Kutas took advantage of the a/an English indefinite article phonological alternation and the sensitivities of the N400 ERP component to show that readers can neurally preactivate individual words of a sentence (including nouns and their prenominal indefinite articles) in a graded fashion with a likelihood estimated from the words' offline probabilities as sentence c...

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...

This paper analyzes the narrative event cloze test and its recent evolution. The test removes one event from a document’s chain of events, and systems predict the missing event. Originally proposed to evaluate learned knowledge of event scenarios (e.g., scripts and frames), most recent work now builds ngramlike language models (LM) to beat the test. This paper argues that the test has slowly/un...