35 research outputs found
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
We study a symmetric collaborative dialogue setting in which two agents, each
with private knowledge, must strategically communicate to achieve a common
goal. The open-ended dialogue state in this setting poses new challenges for
existing dialogue systems. We collected a dataset of 11K human-human dialogues,
which exhibits interesting lexical, semantic, and strategic elements. To model
both structured knowledge and unstructured language, we propose a neural model
with dynamic knowledge graph embeddings that evolve as the dialogue progresses.
Automatic and human evaluations show that our model is both more effective at
achieving the goal and more human-like than baseline neural and rule-based
models.Comment: ACL 201
Automatic Flood Detection in Multi-Temporal Sentinel-1 Synthetic Aperture Radar Imagery Using ANN Algorithms
Natural Calamities like floods cause wide-range of damage to human existence as well as substructures. For automatic extraction of flooded area in multi-temporal satellite imagery acquired by Sentinel-1 Synthetic Aperture Radar (SAR), this paper presents two neural network algorithms: Feed-Forward Neural Network, Cascade-forward back-propagation neural network. This work currently focuses on Uttar Pradesh in India, which was affected due to floods during August 2017. The two models are trained, validated and tested using MATLAB R2018b. The models are first trained using a variety of input data until the percentage of error with respect to water body detection is within an acceptable error limit. These models are then used to extract the water features effectively and to detect the flooded regions. Finally, flood area is calculated in sq. km in during flood and post-flood imagery using these algorithms. The results thus obtained are compared with that from the binary thresholding method from previous studies. The results show that the Feed- Forward Neural Network gives better accuracy than the Cascade-forward back propagation neural network. Based on the promising results, the proposed method may assist in our understanding of the role of machine learning in disaster detection
Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue
Traditional recommendation systems produce static rather than interactive
recommendations invariant to a user's specific requests, clarifications, or
current mood, and can suffer from the cold-start problem if their tastes are
unknown. These issues can be alleviated by treating recommendation as an
interactive dialogue task instead, where an expert recommender can sequentially
ask about someone's preferences, react to their requests, and recommend more
appropriate items. In this work, we collect a goal-driven recommendation
dialogue dataset (GoRecDial), which consists of 9,125 dialogue games and 81,260
conversation turns between pairs of human workers recommending movies to each
other. The task is specifically designed as a cooperative game between two
players working towards a quantifiable common goal. We leverage the dataset to
develop an end-to-end dialogue system that can simultaneously converse and
recommend. Models are first trained to imitate the behavior of human players
without considering the task goal itself (supervised training). We then
finetune our models on simulated bot-bot conversations between two paired
pre-trained models (bot-play), in order to achieve the dialogue goal. Our
experiments show that models finetuned with bot-play learn improved dialogue
strategies, reach the dialogue goal more often when paired with a human, and
are rated as more consistent by humans compared to models trained without
bot-play. The dataset and code are publicly available through the ParlAI
framework.Comment: EMNLP 201
A RADIOLOGICAL PROFILE OF FUNGAL SINUSITIS
  Objectives: To create a radiological profile of fungal sinusitis and determine the radiological differences between fungal and nonfungal sinusitis based on the presence of hyperattenuation, bony erosion, neo-osteogenesis, air-fluid level, and extrasinus extension.Methods: This is a retrospective, single-blind, case-control study involving the analysis of 119 computed tomography (CT) scans of the paranasal sinuses. Based on the histopathology, they were divided into cases comprising fungal sinusitis and controls of nonfungal sinusitis. Benign and malignant tumors and previously operated cases of fungal sinusitis were excluded from the study. The principal investigators were blinded to the diagnosis. The comparison parameters were hyperattenuation, the presence of air-fluid level, bone erosion, neo-osteogenesis, and extrasinus extension. Data was analyzed by Chi-square and Fischer exact t-test using SPSS 14.0 software and a p < 0.05 was considered significant.Results: Our study showed the presence of hyperattenuation, neo-osteogenesis, bone erosion, air-fluid level, extrasinus extension in 75.2%, 48.3%, 25.9%, 36.2%, and 6.9% of the cases and 13.1%, 16.4%, 6.6%, 9.8%, and 0 controls, respectively. All the parameters were statistically significant in cases when compared to controls.Conclusion: Hyperattenuation, neo-osteogenesis, air-fluid level, bone erosion, and extrasinus extension are the parameters on CT imaging that will help routinely assess and differentiate fungal sinusitis from nonfungal sinusitis with considerable accuracy, although, there is an overlap with malignancy when the parameter of bone erosion is considered as a differential diagnosis of chronic invasive fungal sinusitis. It reiterates the fact that history, clinical examination, and laboratory evaluation hold an important role in provisional diagnosis
The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding
In a real-world dialogue system, generated responses must satisfy several
interlocking constraints: being informative, truthful, and easy to control. The
two predominant paradigms in language generation -- neural language modeling
and rule-based generation -- both struggle to satisfy these constraints. Even
the best neural models are prone to hallucination and omission of information,
while existing formalisms for rule-based generation make it difficult to write
grammars that are both flexible and fluent. We describe a hybrid architecture
for dialogue response generation that combines the strengths of both
approaches. This architecture has two components. First, a rule-based content
selection model defined using a new formal framework called dataflow
transduction, which uses declarative rules to transduce a dialogue agent's
computations (represented as dataflow graphs) into context-free grammars
representing the space of contextually acceptable responses. Second, a
constrained decoding procedure that uses these grammars to constrain the output
of a neural language model, which selects fluent utterances. The resulting
system outperforms both rule-based and learned approaches in human evaluations
of fluency, relevance, and truthfulness
Mapping geographical inequalities in access to drinking water and sanitation facilities in low-income and middle-income countries, 2000-17
Background: Universal access to safe drinking water and sanitation facilities is an essential human right, recognised in the Sustainable Development Goals as crucial for preventing disease and improving human wellbeing. Comprehensive, high-resolution estimates are important to inform progress towards achieving this goal. We aimed to produce high-resolution geospatial estimates of access to drinking water and sanitation facilities. Methods: We used a Bayesian geostatistical model and data from 600 sources across more than 88 low-income and middle-income countries (LMICs) to estimate access to drinking water and sanitation facilities on continuous continent-wide surfaces from 2000 to 2017, and aggregated results to policy-relevant administrative units. We estimated mutually exclusive and collectively exhaustive subcategories of facilities for drinking water (piped water on or off premises, other improved facilities, unimproved, and surface water) and sanitation facilities (septic or sewer sanitation, other improved, unimproved, and open defecation) with use of ordinal regression. We also estimated the number of diarrhoeal deaths in children younger than 5 years attributed to unsafe facilities and estimated deaths that were averted by increased access to safe facilities in 2017, and analysed geographical inequality in access within LMICs. Findings: Across LMICs, access to both piped water and improved water overall increased between 2000 and 2017, with progress varying spatially. For piped water, the safest water facility type, access increased from 40·0% (95% uncertainty interval [UI] 39·4–40·7) to 50·3% (50·0–50·5), but was lowest in sub-Saharan Africa, where access to piped water was mostly concentrated in urban centres. Access to both sewer or septic sanitation and improved sanitation overall also increased across all LMICs during the study period. For sewer or septic sanitation, access was 46·3% (95% UI 46·1–46·5) in 2017, compared with 28·7% (28·5–29·0) in 2000. Although some units improved access to the safest drinking water or sanitation facilities since 2000, a large absolute number of people continued to not have access in several units with high access to such facilities (>80%) in 2017. More than 253 000 people did not have access to sewer or septic sanitation facilities in the city of Harare, Zimbabwe, despite 88·6% (95% UI 87·2–89·7) access overall. Many units were able to transition from the least safe facilities in 2000 to safe facilities by 2017; for units in which populations primarily practised open defecation in 2000, 686 (95% UI 664–711) of the 1830 (1797–1863) units transitioned to the use of improved sanitation. Geographical disparities in access to improved water across units decreased in 76·1% (95% UI 71·6–80·7) of countries from 2000 to 2017, and in 53·9% (50·6–59·6) of countries for access to improved sanitation, but remained evident subnationally in most countries in 2017. Interpretation: Our estimates, combined with geospatial trends in diarrhoeal burden, identify where efforts to increase access to safe drinking water and sanitation facilities are most needed. By highlighting areas with successful approaches or in need of targeted interventions, our estimates can enable precision public health to effectively progress towards universal access to safe water and sanitation