Convolutional Bi-directional LSTM for Detecting Inappropriate Query Suggestions in Web Search

Yenala, Harish; Chinnakotla, Manoj Kumar; Goyal, Jay

doi:10.1007/978-3-319-57454-7_1

Cited by 11 publications

(9 citation statements)

References 14 publications

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“…This framework combines CNN and double-layer LSTM for 3D shape segmentation, which can output the edge image of each defined view. Harish et al [45] used the model combining CNN and BiLSTM to automatically identify inappropriate query suggestions, and the performance of this model is better than that of multiple benchmark models using the same data set for training. The model used in this study is based on the above research and further improved according to the research needs.…”

Section: Lstm Bilstm and C-bilstmmentioning

confidence: 99%

A C-BiLSTM Approach to Classify Construction Accident Reports

Hou

Deng

et al. 2020

Applied Sciences

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The construction sector is widely recognized as having the most hazardous working environment among the various business sectors, and many research studies have focused on injury prevention strategies for use on construction sites. The risk-based theory emphasizes the analysis of accident causes extracted from accident reports to understand, predict, and prevent the occurrence of construction accidents. The first step in the analysis is to classify the incidents from a massive number of reports into different cause categories, a task which is usually performed on a manual basis by domain experts. The research described in this paper proposes a convolutional bidirectional long short-term memory (C-BiLSTM)-based method to automatically classify construction accident reports. The proposed approach was applied on a dataset of construction accident narratives obtained from the Occupational Safety and Health Administration website, and the results indicate that this model performs better than some of the classic machine learning models commonly used in classification tasks, including support vector machine (SVM), naïve Bayes (NB), and logistic regression (LR). The results of this study can help safety managers to develop risk management strategies.

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Section: Lstm Bilstm and C-bilstmmentioning

confidence: 99%

A C-BiLSTM Approach to Classify Construction Accident Reports

Hou

Deng

et al. 2020

Applied Sciences

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“…Working definition. For our discussion here-to incorporate aspects of problematic suggestions mentioned by prior work e.g., (Diakopoulos 2013b;Cheung 2015;Yenala, Chinnakotla, and Goyal 2017;Elers 2014)-we follow (Olteanu, Diaz, and Kazai 2020) and broadly consider problematic any suggestion that may be unexpectedly offensive, discriminatory, or biased, or that may promote deceit, misinformation or content that is in some other way harmful (including adult, violent or suicidal content). Problematic suggestions may reinforce stereotypes, or may nudge users towards harmful or questionable patterns of behaviour.…”

Section: Characterizationmentioning

confidence: 99%

“…Understanding which suggestions should be construed as problematic and how to efficiently detect them also requires examining possible dimensions of problematic suggestions such as their content (e.g., what type of content or topics are more likely to be perceived as problematic?) (Olteanu, Diaz, and Kazai 2020;Miller and Record 2017;Yenala, Chinnakotla, and Goyal 2017), targets (e.g., who or what is more likely to be referenced in problematic queries?) (Olteanu, Diaz, and Kazai 2020;Olteanu, Castillo, Boy, et al 2018; UN Women 2013), structure (e.g., are problematic queries likely to be written in a certain way?)…”

Section: Dimensions Of Problematic Suggestionsmentioning

confidence: 99%

“…While existing work has mainly focused on the content of the queries, such as recognizing profane, racist, or sexist content (e.g., (Yenala, Chinnakotla, and Goyal 2017)), other types of problematic suggestions like subtle stereotypical beliefs have often been overlooked. We are interested in topics and content categories which if expressed through a suggestion, then that suggestion is likely to be problematic.…”

Section: Contentmentioning

confidence: 99%

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On the Social and Technical Challenges of Web Search Autosuggestion Moderation

Hazen,

Olteanu,

Kazai

et al. 2020

Preprint

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Past research shows that users benefit from systems that support them in their writing and exploration tasks. The autosuggestion feature of Web search engines is an example of such a system: It helps users in formulating their queries by offering a list of suggestions as they type. Autosuggestions are typically generated by machine learning (ML) systems trained on a corpus of search logs and document representations. Such automated methods can become prone to issues that result in problematic suggestions that are biased, racist, sexist or in other ways inappropriate. While current search engines have become increasingly proficient at suppressing such problematic suggestions, there are still persistent issues that remain. In this paper, we reflect on past efforts and on why certain issues still linger by covering explored solutions along a prototypical pipeline for identifying, detecting, and addressing problematic autosuggestions. To showcase their complexity, we discuss several dimensions of problematic suggestions, difficult issues along the pipeline, and why our discussion applies to the increasing number of applications beyond web search that implement similar textual suggestion features. By outlining persistent social and technical challenges in moderating web search suggestions, we provide a renewed call for action.

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“…The problem of detecting offensive utterances in conversations is wrought with challenges such as handling natural language ambiguity, rampant use of spelling mistakes and variations for abusive and offensive terms and disambiguating with context and other entity names such as pop songs which usually have abusive terms in them (Chen et al, 2012). For this task, we experimented with several approaches, and found Ruuh's current neural Bi-directional LSTM based model (Yenala et al, 2017) to perform the best.…”

Section: Detecting Offensive Conversationsmentioning

confidence: 99%

Insights from Building an Open-Ended Conversational Agent

Gupta¹,

Joshi²,

Chatterjee³

et al. 2019

Proceedings of the First Workshop on NLP for Conversational AI

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Dialogue systems and conversational agents are becoming increasingly popular in modern society. We conceptualized one such conversational agent, Microsoft's "Ruuh" with the promise to be able to talk to its users on any subject they choose. Building an open-ended conversational agent like Ruuh at onset seems like a daunting task, since the agent needs to think beyond the utilitarian notion of merely generating "relevant" responses and meet a wider range of user social needs, like expressing happiness when user's favourite sports team wins, sharing a cute comment on showing the pictures of the user's pet and so on. The agent also needs to detect and respond to abusive language, sensitive topics and trolling behaviour of the users. Many of these problems pose significant research challenges as well as product design limitations as one needs to circumnavigate the technical limitations to create an acceptable user experience. However, as the product reaches the real users the true test begins, and one realizes the challenges and opportunities that lie in the vast domain of conversations. With over 2.5 million real-world users till date who have generated over 300 million user conversations with Ruuh, there is a plethora of learning, insights and opportunities that we will talk about in this paper.

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Convolutional Bi-directional LSTM for Detecting Inappropriate Query Suggestions in Web Search

Cited by 11 publications

References 14 publications

A C-BiLSTM Approach to Classify Construction Accident Reports

A C-BiLSTM Approach to Classify Construction Accident Reports

On the Social and Technical Challenges of Web Search Autosuggestion Moderation

Insights from Building an Open-Ended Conversational Agent

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