Title Generation with Quasi-Synchronous Grammar

The task of selecting information and rendering it appropriately appears in multiple contexts in summarization. In this paper we present a model that simultaneously optimizes selection and rendering preferences. The model operates over a phrase-based representation of the source document which we obtain by merging PCFG parse trees and dependency graphs. Selection preferences for individual phrases are learned discriminatively, while a quasi-synchronous grammar (Smith and Eisner, 2006) captures rendering preferences such as paraphrases and compressions. Based on an integer linear programming formulation, the model learns to generate summaries that satisfy both types of preferences, while ensuring that length, topic coverage and grammar constraints are met. Experiments on headline and image caption generation show that our method obtains state-of-the-art performance using essentially the same model for both tasks without any major modifications.

A Neural Attention Model for Abstractive Sentence Summarization

Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method utilizes a local attention-based model that generates each word of the summary conditioned on the input sentence. While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data. The model shows significant performance gains on the DUC-2004 shared task compared with several strong baselines.Comment: Proceedings of EMNLP 201

Comparison of Language Models by Stochastic Context-Free Grammar, Bigram and Quasi-Simplified-Trigram

In this paper, we investigate the language models by stochasic context-free grammar (SCFG), bigram and quasi-trigram. For calculating of statistics of bigram and quasi-trigram, we used the set of sentences generated randomly from CFG that are legal in terms of semantics. We compared them on the perplexities for their models and the sentence recognition accuracies. The sentence recognition was experimented in the "UNIX-QA" task with the vocabulary size of 521 words. From these results, the perplexities of bigram and quasi-trigram were about 1.6 times and 1.3 times larger than the perplexity of CFG that corresponds to the most restricted grammar (perplexity=10.0), and the perplexity of SCFG is only about 1/2 of CFG. We realized that quasi-trigram had the almost same ability of modeling as the most restricted CFG when the set of plausible sentences in the task was given

A Survey of Paraphrasing and Textual Entailment Methods

Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of Informatics, Athens University of Economics and Business, Greece, 201

THE EFFECTIVENESS OF SOCIAL MEDIA ACTIVITIES ON TAIWANESE UNDERGRADUATES' EFL GRAMMAR ACHIEVEMENT

The purpose of this study was to compare the effects of social media language learning activities with traditional language learning activities on the development of L2 grammatical competence in two English as a Foreign Language (EFL) classes at a Taiwanese university. The study was grounded in four bodies of knowledge: (a) the Input-Interaction-Output (IIO) model (Block, 2003); (b) the sociocultural/activity theory (Lantolf, 2000); (c) current L2 grammar learning theory (Ellis, 2006); and (d) computer-assisted language learning (CALL) theory (Levy & Stockwell, 2006). A convenience sample of 84 Taiwanese undergraduate students officially enrolled in the college voluntarily participated in the study. A quasi-experimental pretest/posttest design was utilized. An ANCOVA was conducted to assess whether collaborative social media activities can bring about significantly better outcomes regarding EFL grammar usage. Results indicated that the treatment group significantly outperformed the control group when controlling for pre-existing knowledge. Results also indicated that there was a significant difference in students' time devoted to English grammar activities between the treatment group and the control group in favor of the treatment group. Furthermore, there was a statistically significant relationship between the time spent on wiki sites and students' English grammar achievement gains. The time students in the treatment group spent on grammar activities increased when they used the social media, and they self-reported spending more time on task during free time. Overall, treatment group students' devotion to the social media activities brought about effective peer support and collaborative learning

A Systematic Review: Incorporating Social Media Tools Into Language Learning

This study highlights a segment of a study that explores the incorporating the social media tools into language learning. These social media tools such as Twitter, Facebook, and Instagram...etc. have become such an intriguing social media that there is a growing need among educationalists, learners, and administrators to explore their impact and effectiveness in the field of language learning. Objective: An updated systematic review was carried out of research studies looking at the incorporating of social media into language learning. Methods: We conducted a systematic literature review on empirical research regarding the incorporating and effectiveness of social media into language learning. The studies we included met some specific criteria as well as collected from different databases. Besides that, the study used the CASP checklist and the guidelines set by PRISMA for choosing the eligible studies that related to the systematic review purpose. The data were extracted, and results were categorized into four themes then summarized using a narrative. Results: Initially, a total of 1,085 articles were identified from which 21 were included in the study. From these articles four, themes were applied as the following: the first them is the studies numbers with the publication year which showed that using social media into language learning become more popular around the years between 2014 till 2016. The second them is social media tools with language skills which showed the variety of using social media tools such as Facebook, Instagram, Twitter, and WhatsApp among the language skills. The third them identified the most methods that used in different studies to be approved the use of social media into language learning. So, it was apparent that experimental study with the pre-test and post-test design was most used in the studies. The last them is the effectiveness of using social media in language learning which approved that using social media was effective in most studies for improving different language skills. Conclusion: Our review provides insights into the emerging utilization of social media in language learning. In particular, it identifies types of social media tools that used the most to improve the language skills as well as the effects of such use, which may differ between skill to skill. Accordingly, our results framework and propositions can serve to guide future research, and they also have practical implications for language learning and developing the design instruction. Key Words: Social media, Language learning, Systematic review, Language skill

Languaging School into Being : A Discourse Analysis of Online ELA Classes Within the Context of the COVID-19 Pandemic

At the beginning of the COVID-19 pandemic, school buildings across the United States shut their doors and transitioned students and teachers to remote learning, most often utilizing internet-based technology to provide either asynchronous or synchronous lessons. I was a high school English Language Arts teacher in Stone Valley School District in Northeastern New Jersey when the unprecedented school closures moved my classes online for the remainder of the 2019-2020 school year. As a teacher researcher who specialized in New Literacy Studies, I was particularly sensitive to how students and I used technology to continue lessons after the school building shut its doors. At first, students and I interfaced using the multimedia components of the BigBlueButton platform, an interface which my school district had mandated that teachers use to host synchronous classroom lessons. Soon enough, however, I noticed that students were more frequently turning off their cameras and microphones, sitting in unseen silence on the other end of their school-issued laptops; however, as the cameras and microphones were turned off, the Public Chat box came to life as students began to write messages as their means of participating in class. Without school buildings, classrooms, whiteboards, classroom desks, passing time, or athletics, “school” nevertheless continued on. I came to the realization that the pandemic had yielded a unique circumstance—a critical instance—during which a teacher researcher could explore the fundamental components of what made “school” (i.e., the institution of school) into what it was. Furthermore, since school now comprised, nearly entirely, dialogue between myself and my students, I started to conceive of school as something “languaged into being” by individuals who were interacting in roles along certain ways with words. I began to save the Public Chat transcripts, email messages, and notes pages that emerged from 47 synchronous sessions for three Grade 10 English Language Arts classes from March to June 2020. Using discourse analysis to unpack the ways in which language was used in the Public Chat, I found that students and I had indeed made discursive moves that languaged school into being. Students, for example, wrote in ways that positioned themselves to appear to me as “good” students, those who show to the teacher compliance, achievement, and perceived intelligence. Both students and I also seemed to write under the assumption of routinized habits and routines according to what we believed an English Language Arts class to be. Even when students used non-standard or untraditional discursive moves (e.g., emoji), they did so in ways that anchored them to the curriculum. And in the case of a student who used an expletive in class, it was other students who admonished him and circumscribed his behavior. Although language was how school appeared to be conjured into being through the dialectic among students and me—as might be expected from a social constructivist epistemology—there were also deeper structures at play that, perhaps, manifested the linguistic moves. The limitations and design of BigBlueButton interface, for example, reproduced traditional classroom learning styles rather than harnessing the full extent of the internet’s capabilities. Buoyed by counternarratives in the media about failing schools and ‘learning loss’ during the pandemic, an adherence to schedules, deadlines, and curricula strongly continued to reify school grades as important markers of success for my students. Furthermore, what I have called social routines—ways in which individuals habitually interact with tools and technology (broadly encompassing both new and old forms of technology)—manifested certain ways of engaging in roles, such as teacher and student. With the initial lockdown of the COVID-19 pandemic now in the past, fully online classes for public high school students have become an anomaly of a particular critical instance in history in retrospect. Still, the ways that students and their teacher interacted during these lessons as seen in the discursive moves that people use to language school into being, sheds light on the deeper structures and social routines of schooling that operate on a daily basis. Such insights may help future researchers, whether they examine in person or online schools, to identify social routines, mappable through discourse analysis, that individuals perform as ways of taking part in the educational system. This may be of particular interest for demographics in which these discursive moves and social routines do not appear, for it suggests that there are particular ways of using language that perpetuate the institution of school. Individuals who are predisposed to these habits and routines may be better able to succeed in schools, for they can not only anticipate what is to come in classes, but they also work synergistically with teachers to literally bring a certain kind of education into existence

Controlling Output Length in Neural Encoder-Decoders

Neural encoder-decoder models have shown great success in many sequence generation tasks. However, previous work has not investigated situations in which we would like to control the length of encoder-decoder outputs. This capability is crucial for applications such as text summarization, in which we have to generate concise summaries with a desired length. In this paper, we propose methods for controlling the output sequence length for neural encoder-decoder models: two decoding-based methods and two learning-based methods. Results show that our learning-based methods have the capability to control length without degrading summary quality in a summarization task.Comment: 11 pages. To appear in EMNLP 201