[PDF] Top 20 Adversarial Domain Adaptation for Machine Reading Comprehension

... As an additional experiment, we also evalu- ate the proposed AdaMRC framework for semi- supervised domain adaptation. We randomly sam- ple k portion of labeled data from the target do- main, and feed them ... See full document

... One focus of the task is the ability to extract different types of knowledge and to combine them as a way to answer the questions. In order to allow systems to acquire the same background knowledge, ad-hoc collections ... See full document

... To improve the interpretability of question answer- ing, previous work utilize interpretable internal representations (Palangi et al., 2017) or reasoning networks that employ a hop-by-hop reasoning pro- cess dynamically ... See full document

... of reading comprehension tests for MT evaluation has been addressed in previous ...the machine translated documents and that their accuracy is also inferior compared to human translated ...Arabic ... See full document

... We introduce Stochastic Answer Networks (SAN), a simple yet robust model for machine reading comprehension. The use of stochastic dropout in training and averaging in test at the answer module leads ... See full document

... on Machine Reading Comprehension (MRC) with the help of open-source datasets, recent studies indicate that most of the current MRC systems unfor- tunately suffer from weak robustness against ... See full document

... a domain such as Machine Reading Comprehension (MRC) where recent advances in language-modeling based pre- trained embeddings like ELMO (Peters et ... See full document

... on reading strate- gies for improved comprehension levels of human readers, we propose three strategies ...forth reading, highlighting, and self-assessment), aiming at improving machine ... See full document

... Chinese machine reading compre- hension datasets. In cloze-style reading compre- hension, Cui et ...cloze-style reading comprehension dataset: Peo- ple’s Daily & Children’s Fairy ... See full document

... Not surprisingly, we only received three participant sys- tems in User Query Track, as it is much difficult than Cloze Track. As shown in Table 3, the test set performance is significantly lower than that of Cloze Track, ... See full document

... The first text meeting the length requirement was selected 90% of the time and the second text meeting the requirement selected otherwise. For Stack Exchange, the text was a sentence from a post, and for news, where ... See full document

... This table shows close inter-relatedness among subjective and objective assessment. The concept of ‘home’ does not confirm the entire DIs logically coordinate to FVs. While the difficulty indices (from class I-V) of ... See full document

... be improved by incorporating external resources. In Figure 3, we present a sample paragraph and its corresponding answerable questions and gen- erated unanswerable questions. In the first ex- ample, two models generate ... See full document

... Machine Reading Comprehension (MRC), as the name suggests, requires a machine to read a pas- sage and answer its relevant questions. Since the answer to each question is supposed to stem from ... See full document

... Starting from random parameters for both mod- els, we alternate between optimizing the weighted NMT objective in Equation 7, and the classifier’s cross-entropy objective. Training the two concur- rently allows the ... See full document

... Resources for implicit discourse relations are scarce compared to the explicit ones, since they are harder to annotate (Miltsakaki et al., 2004). For example, among corpora annotated with dis- course relations such as ... See full document

... human’s reading comprehension, which has not been well considered in existing machine reading comprehension (MRC) ...chine reading comprehension models by con- sidering ... See full document

... teaching reading is to develop students’ skills so that they can read English texts effectively and ...efficient reading is always purposeful and tends to focus mainly on the purpose of the ...of ... See full document

... for Reading Comprehension. Neural reading models typically leverage various attention mecha- nisms to build interdependent representations of passage and question, and sequentially predict the answer ... See full document

... where the value of d represents how similar the two tweets are. We used k-d tree data structure (Bentley, 1975) to efficiently find the nearest in- stances. To construct the graph, we first represent each tweet by ... See full document