One Engine to Fuzz ’em All: Generic Language Processor Testing with Semantic Validation

2021 ◽  
Author(s):  
Yongheng Chen ◽  
Rui Zhong ◽  
Hong Hu ◽  
Hangfan Zhang ◽  
Yupeng Yang ◽  
...  
Keyword(s):  
Language Processor ◽  
Generic Language

scholarly journals When hearsay trumps evidence: How generic language guides preschoolers’ inferences about unfamiliar things

2008 ◽  
Vol 23 (5) ◽  
pp. 749-766 ◽  
Author(s):  
Craig G. Chambers ◽  
Susan A. Graham ◽  
Juanita N. Turner
Keyword(s):  
Generic Language

scholarly journals Generic Language in Parent-Child Conversations

2008 ◽  
Vol 4 (1) ◽  
pp. 1-31 ◽  
Author(s):  
Susan A. Gelman ◽  
Peggy J. Goetz ◽  
Barbara W. Sarnecka ◽  
Jonathan Flukes
Keyword(s):  
Generic Language ◽  
Parent Child

Generics in society

2021 ◽  
Vol 50 (4) ◽  
pp. 517-532
Author(s):  
Susan A. Gelman
Keyword(s):  
Academic Writing ◽  
Social Life ◽  
Meaning Making ◽  
Social Contexts ◽  
Anthropological Research ◽  
Generic Language ◽  
Contemporary Work ◽  
Positive Functions ◽  
Context Free

ABSTRACTThis article examines two interrelated issues: (i) how considering generics within their social contexts of use contributes to theories of generics, and (ii) how contemporary work on generics provides promising directions for the study of language as an aspect of social life. Examining the function of generics in meaningful interactions stands in contrast to standard treatments, which consider generics as isolated, context-free propositions. Additionally, recent psychological approaches suggest new questions that can enrich sociolinguistic and linguistic anthropological research. These include, for example, when and why generics serve not just negative functions (such as stereotyping) but also positive functions (such as meaning-making), how generics gain their power from what is unstated as opposed to stated, and how generic language distorts academic writing. Ultimately, the study of language in society has the potential to enrich the study of generics beyond what has been learned from their study in linguistics, philosophy, and psychology. (Generics, concepts, categories, stereotyping, induction)*

Contextualized MALL in Target and Non-Target Countries

2019 ◽  
pp. 170-191
Author(s):  
Anat Cohen ◽  
Orit Ezra
Keyword(s):  
Language Learning ◽  
Real World ◽  
Real Life ◽  
Evaluation Index ◽  
Learning Orientation ◽  
Evaluation Tool ◽  
Mobile Assisted Language Learning ◽  
Generic Language ◽  
Life Context ◽  
Target Country

Mobile-assisted language learning (MALL) is known to be conducive to contextualized language learning. However, the literature lacks an understanding of context. Nor is there a quantitative evaluation tool. The chapter's dependent contextualized variables were the following: real-world context level (one's place), real-life context level (one's life), and device mobility (place diversity). The independent variables were target/non-target country (Taiwan/Israel) and language learning orientation (dedicated/generic). In the target country, the spoken language is the studied language. Dedicated/generic language learning orientation represents the existence/non-existence of pedagogy within activities. Fifty-three Chinese L2 students involved in 296 activities and 519 events were interviewed. Using device mobility and an evaluation index developed to measure real-world/real-life context levels, MALL was found more contextualized in Taiwan only in generic activities. The findings refine our understanding of the benefits of studying in the target country. The index can be used in future studies.

Writing Well About Science: Techniques From Teachers of Science Writing

2005 ◽  
Keyword(s):  
Science Writing ◽  
Basic Approach ◽  
Best Friend ◽  
Good Story ◽  
Generic Language

1. Read your work out loud. You will be able to hear rhythm and flow of language this way, and you really cannot hear it when reading silently. 2. Don't be shy. Ask other writers to read a draft for you. Everyone gets too close to the story to see the glitches, and a dispassionate reader is a writer's best friend. Good writers gather readers around them for everything from newspaper stories to whole books (which require really good friends). 3. Think of your lead as seduction. How are you going to get this wary, perhaps uninterested reader, upstairs to see your etchings? You need to begin your story in a way that pulls the reader in. My favorite basic approach goes seductive lead, so-what section (why am I reading this), map section (here are the main points that will follow in this story). That approach leads me to my next tip, which is 4. Have a dear sense of your story and its structure before you begin writing. If you think of a story as an arc, in the shape of a rainbow, then it's helpful to know where it will begin and where it will end so that you know in advance how to build that arc. 5. Use transitions. A story has to flow. Leaping from place to place like a waterstrider on a pond will not make your prose easy to follow. 6. Use analogies. They are a beautiful way to make science vivid and real—as long as you don't overuse them. 7. In fact, don't overwrite at all. And never, never, never use clichés. If you want to write in your voice, generic language will not do. In my class, there are no silver linings, no cats let out of bags, no nights as black as pitch. A student who uses three clichés in a story gets an automatic C from me. 8. Write in English. This applies not only to science writing but to all beats in which a good story can easily sink in a sea of jargon.

scholarly journals Investigation of Spoken-Language Detection and Classification in Broadcasted Audio Content

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 211 ◽  
Author(s):  
Rigas Kotsakis ◽  
Maria Matsiola ◽  
George Kalliris ◽  
Charalampos Dimoulas
Keyword(s):  
Data Augmentation ◽  
Spoken Language ◽  
Language Recognition ◽  
Adaptive Classification ◽  
Audio Recordings ◽  
Generic Language ◽  
Media Monitoring ◽  
Language Detection ◽  
Specific Radio ◽  
Language Classification

The current paper focuses on the investigation of spoken-language classification in audio broadcasting content. The approach reflects a real-word scenario, encountered in modern media/monitoring organizations, where semi-automated indexing/documentation is deployed, which could be facilitated by the proposed language detection preprocessing. Multilingual audio recordings of specific radio streams are formed into a small dataset, which is used for the adaptive classification experiments, without seeking—at this step—for a generic language recognition model. Specifically, hierarchical discrimination schemes are followed to separate voice signals before classifying the spoken languages. Supervised and unsupervised machine learning is utilized at various windowing configurations to test the validity of our hypothesis. Besides the analysis of the achieved recognition scores (partial and overall), late integration models are proposed for semi-automatically annotation of new audio recordings. Hence, data augmentation mechanisms are offered, aiming at gradually formulating a Generic Audio Language Classification Repository. This database constitutes a program-adaptive collection that, beside the self-indexing metadata mechanisms, could facilitate generic language classification models in the future, through state-of-art techniques like deep learning. This approach matches the investigatory inception of the project, which seeks for indicators that could be applied in a second step with a larger dataset and/or an already pre-trained model, with the purpose to deliver overall results.

scholarly journals A Hybrid Spoken Language Processing System for Smart Device Troubleshooting

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 681 ◽  
Author(s):  
Praveen Edward James ◽  
Hou Kit Mun ◽  
Chockalingam Aravind Vaithilingam
Keyword(s):  
Language Processing ◽  
Short Term Memory ◽  
Processing System ◽  
Memory Cell ◽  
Spoken Language ◽  
Smart Device ◽  
Short Term ◽  
Language Processor ◽  
Spoken Language Processing ◽  
Long Short Term Memory

The purpose of this work is to develop a spoken language processing system for smart device troubleshooting using human-machine interaction. This system combines a software Bidirectional Long Short Term Memory Cell (BLSTM)-based speech recognizer and a hardware LSTM-based language processor for Natural Language Processing (NLP) using the serial RS232 interface. Mel Frequency Cepstral Coefficient (MFCC)-based feature vectors from the speech signal are directly input into a BLSTM network. A dropout layer is added to the BLSTM layer to reduce over-fitting and improve robustness. The speech recognition component is a combination of an acoustic modeler, pronunciation dictionary, and a BLSTM network for generating query text, and executes in real time with an 81.5% Word Error Rate (WER) and average training time of 45 s. The language processor comprises a vectorizer, lookup dictionary, key encoder, Long Short Term Memory Cell (LSTM)-based training and prediction network, and dialogue manager, and transforms query intent to generate response text with a processing time of 0.59 s, 5% hardware utilization, and an F1 score of 95.2%. The proposed system has a 4.17% decrease in accuracy compared with existing systems. The existing systems use parallel processing and high-speed cache memories to perform additional training, which improves the accuracy. However, the performance of the language processor has a 36.7% decrease in processing time and 50% decrease in hardware utilization, making it suitable for troubleshooting smart devices.

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