Query Resolution for Conversational Search with Limited Supervision

In this work we focus on multi-turn passage retrieval as a crucial component of conversational search. One of the key challenges in multi-turn passage retrieval comes from the fact that the current turn query is often underspecified due to zero anaphora, topic change, or topic return. Context from the conversational history can be used to arrive at a better expression of the current turn query, defined as the task of query resolution. In this paper, we model the query resolution task as a binary term classification problem: for each term appearing in the previous turns of the conversation decide whether to add it to the current turn query or not. We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers. We propose a distant supervision method to automatically generate training data by using query-passage relevance labels. Such labels are often readily available in a collection either as human annotations or inferred from user interactions. We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC. We incorporate QuReTeC in a multi-turn, multi-stage passage retrieval architecture and demonstrate its effectiveness on the TREC CAsT dataset.Comment: SIGIR 2020 full conference pape

Stevens Johnson syndrome following paraquat poisoning: a case report

Paraquat is a herbicidal agent used extensively, mainly in developing countries where there is a high incidence of its poisoning. It causes damage to kidneys, lungs and liver. Reports of mucocutaneous manifestations following paraquat ingestion are rare. Here we describe a case of Stevens-Johnson syndrome(SJS) presenting in a case of paraquat ingestion. A 22 year old male was admitted to our hospital for difficulty in swallowing and micturation since ingestion of 10-15 ml of paraquat 7 days before. He had multiple hemorrhagic crusted erosions over lips and left maxillary area with diffuse erythematous erosions over bilateral buccal mucosa, palate, labial mucosa and urethral mucosa with whitish slough over them. Upper GI endoscopy revealed oral, esophageal and fundal sloughing. Patient was treated with oral corticosteroids and antibiotics which caused complete resolution of skin lesions within 15 days.Paraquat dichloride exerts its toxicity by generation of reactive oxygen species. Skin lesions following topical application of paraquat are common, but very few cases have been reported of the same after oral ingestion. SJS is caused by a variety of drugs and commonly presents with muco-cutaneous tenderness, hemorrhagic erosions and erythematous macules with 90% developing oral, genital and gastrointestinal mucosal involvement. As the oral and genital manifestations in our patient developed the day after paraquat ingestion, possibility of SJS developing due to the same are the highest. Paraquat should not be ruled out as a drug causality if mucocutaneous manifestations of SJS/TEN appear in a patient of paraquat ingestion.

Embedding Principal Component Analysis for Data Reductionin Structural Health Monitoring on Low-Cost IoT Gateways

Principal component analysis (PCA) is a powerful data reductionmethod for Structural Health Monitoring. However, its computa-tional cost and data memory footprint pose a significant challengewhen PCA has to run on limited capability embedded platformsin low-cost IoT gateways. This paper presents a memory-efficientparallel implementation of the streaming History PCA algorithm.On our dataset, it achieves 10x compression factor and 59x memoryreduction with less than 0.15 dB degradation in the reconstructedsignal-to-noise ratio (RSNR) compared to standard PCA. More-over, the algorithm benefits from parallelization on multiple cores,achieving a maximum speedup of 4.8x on Samsung ARTIK 710

Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games

The video game industry has adopted recommendation systems to boost users interest with a focus on game sales. Other exciting applications within video games are those that help the player make decisions that would maximize their playing experience, which is a desirable feature in real-time strategy video games such as Multiplayer Online Battle Arena (MOBA) like as DotA and LoL. Among these tasks, the recommendation of items is challenging, given both the contextual nature of the game and how it exposes the dependence on the formation of each team. Existing works on this topic do not take advantage of all the available contextual match data and dismiss potentially valuable information. To address this problem we develop TTIR, a contextual recommender model derived from the Transformer neural architecture that suggests a set of items to every team member, based on the contexts of teams and roles that describe the match. TTIR outperforms several approaches and provides interpretable recommendations through visualization of attention weights. Our evaluation indicates that both the Transformer architecture and the contextual information are essential to get the best results for this item recommendation task. Furthermore, a preliminary user survey indicates the usefulness of attention weights for explaining recommendations as well as ideas for future work. The code and dataset are available at: https://github.com/ojedaf/IC-TIR-Lol

Attentive History Selection for Conversational Question Answering

Conversational question answering (ConvQA) is a simplified but concrete setting of conversational search. One of its major challenges is to leverage the conversation history to understand and answer the current question. In this work, we propose a novel solution for ConvQA that involves three aspects. First, we propose a positional history answer embedding method to encode conversation history with position information using BERT in a natural way. BERT is a powerful technique for text representation. Second, we design a history attention mechanism (HAM) to conduct a "soft selection" for conversation histories. This method attends to history turns with different weights based on how helpful they are on answering the current question. Third, in addition to handling conversation history, we take advantage of multi-task learning (MTL) to do answer prediction along with another essential conversation task (dialog act prediction) using a uniform model architecture. MTL is able to learn more expressive and generic representations to improve the performance of ConvQA. We demonstrate the effectiveness of our model with extensive experimental evaluations on QuAC, a large-scale ConvQA dataset. We show that position information plays an important role in conversation history modeling. We also visualize the history attention and provide new insights into conversation history understanding.Comment: Accepted to CIKM 201

Antibiotic removal processes from water & wastewater for the protection of the aquatic environment - a review

Currently the serious problem of contamination by antibiotics is a reality. The scientific evidence of its negative effects on the aquatic environment and human health are numerous and unquestionable. Therefore, it is essential to intensify research into effective and efficient processes for removing antibiotics from the aquatic environment. In this paper, on the one hand, a review of the concentrations detected in all types of waters of some antibiotics is developed. In concrete of Ciprofloxacin (CIP), Erythromycin (ERY), Levofloxacin (LEV), Metronidazole (MET), Norfloxacin (NOR), Ofloxacin (OFL), Sulfamethoxazole (SMX) and Trimethoprim (TIM). Of the publications consulted, it can be noted that the most detected is SMX, while those with the highest concentrations are CIP, SMX and TIM. On the other hand, some of the main methods to eliminate antibiotics from the aquatic environment are defined and classified. The methods are compared, indicating their advantages and disadvantages. Combined processes are also mentioned as a good alternative. Finally, the removal percentages achieved by each method in some representative publications are detailed. In this regard, it can be said that the methods with the best elimination percentages (range 80–100%) are biological methods (Biological Aerated Filter, Anaerobic Digestion & Biological Activated Carbon Filter) and membrane technology (Nanofiltration & Reverse Osmosis). While those with the worst results (under 80%) are chemicals (Coagulation-Flocculation) and constructed wetlands (Horizontal Subsurface Flow Constructed Wetlands)

Open-Retrieval Conversational Question Answering

Conversational search is one of the ultimate goals of information retrieval. Recent research approaches conversational search by simplified settings of response ranking and conversational question answering, where an answer is either selected from a given candidate set or extracted from a given passage. These simplifications neglect the fundamental role of retrieval in conversational search. To address this limitation, we introduce an open-retrieval conversational question answering (ORConvQA) setting, where we learn to retrieve evidence from a large collection before extracting answers, as a further step towards building functional conversational search systems. We create a dataset, OR-QuAC, to facilitate research on ORConvQA. We build an end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader that are all based on Transformers. Our extensive experiments on OR-QuAC demonstrate that a learnable retriever is crucial for ORConvQA. We further show that our system can make a substantial improvement when we enable history modeling in all system components. Moreover, we show that the reranker component contributes to the model performance by providing a regularization effect. Finally, further in-depth analyses are performed to provide new insights into ORConvQA.Comment: Accepted to SIGIR'2