Meaningful Answer Generation of E-Commerce Question-Answering

In e-commerce portals, generating answers for product-related questions has become a crucial task. In this article, we focus on the task of product-aware answer generation , which learns to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. However, safe answer problems (i.e., neural models tend to generate meaningless and universal answers) pose significant challenges to text generation tasks, and e-commerce question-answering task is no exception. To generate more meaningful answers, in this article, we propose a novel generative neural model, called the Meaningful Product Answer Generator ( MPAG ), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration. Product reviews and product attributes are used to provide meaningful content, while the prototype answer can yield a more diverse answer pattern. To this end, we propose a novel answer generator with a review reasoning module and a prototype answer reader. Our key idea is to obtain the correct question-aware information from a large-scale collection of reviews and learn how to write a coherent and meaningful answer from an existing prototype answer. To be more specific, we propose a read-and-write memory consisting of selective writing units to conduct reasoning among these reviews . We then employ a prototype reader consisting of comprehensive matching to extract the answer skeleton from the prototype answer. Finally, we propose an answer editor to generate the final answer by taking the question and the above parts as input. Conducted on a real-world dataset collected from an e-commerce platform, extensive experimental results show that our model achieves state-of-the-art performance in terms of both automatic metrics and human evaluations. Human evaluation also demonstrates that our model can consistently generate specific and proper answers.

ANALISIS KEMAMPUAN STRUCTURE SENSE DALAM MATERI PEMANGKATAN BENTUK ALJABAR PADA SISWA MTs ASSALAM

This article aims to determine the extent to which the ability to structure sense in the form of material binding a^2-b^2=(a-b)(a+b). This type of research is qualitative research. The subjects of this study were students of class VII-B MTs ASSALAM Bangilan, amounting to 25 students. However, only six subjects were analyzed based on the ability indicators of structure sense in terms of the same answer patterns. The research instrument consisted of test questions and interview guidelines. Data analysis techniques used in the model of Miles and Huberman include data reduction, data presentation, and concluding. The study results showed that in question number 1, 40% of students could sense structure, and 60% are less familiar with the sense of structure due to each deployment's completion, not using the form a^2-b^2=(a-b)(a+b). In question number 2, 32% of students can structure sense, and 68% are less familiar with the sense of structure due to a^2-b^2=(a-b)(a+b) sensitivity is still low, and the answer pattern describes each appointment. Whereas in question number 3, only 28% can structure sense, and 72% lack familiarity with structure sense because they cannot manipulate structures properly. In conclusion, the ability to structure sense to form a^2-b^2=(a-b)(a+b) students is still low. Artikel ini bertujuan untuk mengetahui sejauhmana kemampuan structure sense pada materi pemangakatan bentuk a^2-b^2=(a-b)(a+b). Jenis penelitian ini menggunakan penelitian kualitatif. Subyek penelitian ini adalah siswa kelas VII-B MTs ASSALAM Bangilan yang berjumlah 25 siswa, akan tetapi hanya diambil 6 subyek yang dianalisis berdasarkan indikator kemampuan structure sense ditinjau dari pola jawaban yang sama. Instrumen penelitian terdiri dari soal tes dan pedoman wawancara. Teknik analisis data menggunakan model Miles dan Huberman meliputi reduksi data, penyajian data dan penarikan kesimpulan. Hasil penelitian menunjukkan bahwa pada soal no. 1, 40% siswa memiliki kemampuan structure sense dan 60% kurang mengenal structure sense disebabkan dalam penyelsaiannya menjebarkan masing-masing pemangkatan, tidak menggunakan bentuk a^2-b^2=(a-b)(a+b). Pada soal no. 2, 32% siswa memiliki kemampuan structure sense dan 68% kurang mengenal structure sense disebabkan kepekaan struktur pemangkatan a^2-b^2=(a-b)(a+b) masih rendah, dan pola jawabannya menjabarkan masing-masing pemangkatan. Sedangkan pada soal no. 3 hanya 28% yang memiliki kemampuan structure sense dan 72% kurang mengenal structure sensedisebabkan tidak dapat memanipulasi struktur dengan tepat. Kesimpulannya kemampuan structure sense pemangakata bentuk a^2-b^2=(a-b)(a+b) siswa masih rendah.

Rasch Modeling: A Multiple Choice Chemistry Test

The study aimed to reveal the difficulty level of items and the suitability of items of Chemistry test with the Rasch model. In addition to detecting this item quality, the Rasch model shows the student's answer pattern as well, so that the assessment can imply the quality of the instrument as an assessment of chemical learning. As many as 20 numbers of multiple-choice questions in chemical bonding material were analyzed by using WINSTEPS 3.73. The samples consisted of 200 senior high school students in Banjarmasin Indonesia. The results revealed that the average item measure was 0.00 with items (Measure Order = 4.64) which has the highest difficulty level. The Q10 was the item that has a level of conformity with the model, and outliers or misfit in Rasch were MNSQ=+0.97, ZSTD=-0.2, Pt Mean Corr=+0.58. In other words, assessment of learning with test techniques such as multiple choice based on Rasch model analysis was an effective way for teachers to review the progress of students in the learning process, guidelines for designing chemical learning strategies, and identifying students' understanding of chemical material.

Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition? (Preprint)

BACKGROUND Different rare diseases (RD) obviously result in substantial clinical appearances and diagnostic challenges for health professionals. However, we hypothesized that there are consistencies and shared phenomena among all individuals affected by (different) RD during the time before the diagnosis could be established. OBJECTIVE We aimed to identify communalities between different RD and developed a machine learning diagnostic support tool for RD. METHODS 20 interviews with affected individuals with different RD focusing on the time before their final diagnosis was established were performed and qualitatively analyzed. Out of these pre-diagnostic experiences we distilled key phenomena and created a questionnaire which was then distributed among individuals with the established diagnosis of i.) RD, ii.) common non-rare diseases (NRO) iii.) common chronic diseases (CD), iv.), psychosomatic or somatoform diseases (PSY), individuals. Finally, four combined single data mining methods and a fusion algorithm were trained to distinguish the different answer patterns of the questionnaires. RESULTS The questionnaire contained 53 questions. At the end of the campaign a total sum of 1763 questionnaires (758 RD, 149 CD, 48 PSY, 200 NRO, 34 healthy individuals and 574 not evaluable questionnaires) were collected. Based on 3 independent data sets the 10-fold stratified cross-validation method for the answer-pattern recognition resulted in sensitivity values of 88.9% to detect the answer pattern of a RD, 86.6% for NRO, 87.7% for CD and 84.2% for PSY. CONCLUSIONS Despite of being so different, patients with RD share surprisingly similar pre-diagnosis experiences. These communalities were qualitatively explored and successfully used to develop a questionnaire. Mathematical algorithms learned to distinguish these different answer-patterns. Such a questionnaire-based diagnostic support tool might aid professional medical users to raise suspicion for a RD and it could help to shorten the way to the correct diagnosis. Our questionnaire- and data-mining based approach was successfully able to detect unique patterns in individuals affected by a broad range of different rare diseases. Therefore, this approach may shorten the often observed diagnostic delay in RD.

Positioning the reader

The present contribution analyzes the linguistic realization of the Problem-Solution pattern, the Question-Answer pattern and the Claim-Response pattern (Hoey 2001) in a sample of US written newspaper editorials and opinion articles. The objectives pursued are twofold: to explore how these textual patterns interact in the newspaper opinion genre and to unveil the kind of writer-reader relationship this interaction reflects. To achieve these goals, sample texts were analyzed first for the three textual patterns under consideration, and then the patterns themselves were characterized by identifying their most frequent textual components (e.g. Problem, Solution, Evaluation, Question, Answer, Claim, Affirm, etc.). Findings reveal different types of interaction among text patterns, which respond to different persuasive strategies unfolded by writers to achieve their communicative objectives. Results presented here can be of interest to researchers in discourse analysis and written communication.

On questions following questions in service encounters

ABSTRACTThis paper is a treatment of some patterns of talk that occur in service encounters (and presumably in other conversations as well). It is an attempt to examine the contribution of discourse structures such as the question–answer adjacency pair to the coherence of everyday discourse. The customer-request–server-response sequence is examined as to its adherence to a pattern of question–answer. Though some sequences do adhere to the question–answer pattern, there are many that do not, but rather are manifested as question–question. The question–question patterns are shown to be of several kinds in terms of the relationship of the second question to the first. The analysis demonstrates the relationship of these patterns to the pragmatic interpretation of the customer-request as either a request for information or as a request for service, and leads to a tentative set of generalizations concerning the interpretation of responses to questions in general. (Questions, conversational analysis, discourse analysis, pragmatics, indirect speech acts, coherence, ritual, service encounters, American English.)