scholarly journals Learning Neural Sequence-to-Sequence Models from Weak Feedback with Bipolar Ramp Loss

2019 ◽  
Vol 7 ◽  
pp. 233-248
Author(s):  
Laura Jehl ◽  
Carolin Lawrence ◽  
Stefan Riezler
Keyword(s):  
Machine Learning ◽  
Machine Translation ◽  
Likelihood Estimation ◽  
Semantic Parsing ◽  
Neural Models ◽  
Minimum Risk ◽  
Weak Supervision ◽  
Learning Scenarios ◽  
Ramp Loss ◽  
Weakly Supervised

In many machine learning scenarios, supervision by gold labels is not available and conse quently neural models cannot be trained directly by maximum likelihood estimation. In a weak supervision scenario, metric-augmented objectives can be employed to assign feedback to model outputs, which can be used to extract a supervision signal for training. We present several objectives for two separate weakly supervised tasks, machine translation and semantic parsing. We show that objectives should actively discourage negative outputs in addition to promoting a surrogate gold structure. This notion of bipolarity is naturally present in ramp loss objectives, which we adapt to neural models. We show that bipolar ramp loss objectives outperform other non-bipolar ramp loss objectives and minimum risk training on both weakly supervised tasks, as well as on a supervised machine translation task. Additionally, we introduce a novel token-level ramp loss objective, which is able to outperform even the best sequence-level ramp loss on both weakly supervised tasks.

scholarly journals Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering

2020 ◽  
Author(s):  
Tao Shen ◽  
Xiubo Geng ◽  
Guodong Long ◽  
Jing Jiang ◽  
Chengqi Zhang ◽  
...  
Keyword(s):  
Question Answering ◽  
Logical Form ◽  
Computation Time ◽  
Search Space ◽  
Training Data ◽  
Semantic Parsing ◽  
Weak Supervision ◽  
Knowledge Based ◽  
Model Training ◽  
Weakly Supervised

Many algorithms for Knowledge-Based Question Answering (KBQA) depend on semantic parsing, which translates a question to its logical form. When only weak supervision is provided, it is usually necessary to search valid logical forms for model training. However, a complex question typically involves a huge search space, which creates two main problems: 1) the solutions limited by computation time and memory usually reduce the success rate of the search, and 2) spurious logical forms in the search results degrade the quality of training data. These two problems lead to a poorly-trained semantic parsing model. In this work, we propose an effective search method for weakly supervised KBQA based on operator prediction for questions. With search space constrained by predicted operators, sufficient search paths can be explored, more valid logical forms can be derived, and operators possibly causing spurious logical forms can be avoided. As a result, a larger proportion of questions in a weakly supervised training set are equipped with logical forms, and fewer spurious logical forms are generated. Such high-quality training data directly contributes to a better semantic parsing model. Experimental results on one of the largest KBQA datasets (i.e., CSQA) verify the effectiveness of our approach and deliver a new state-of-the-art performance.

scholarly journals Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences

2018 ◽  
Author(s):  
Jason A. Fries ◽  
Paroma Varma ◽  
Vincent S. Chen ◽  
Ke Xiao ◽  
Heliodoro Tejeda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Aortic Valve ◽  
Cardiac Mri ◽  
Supervised Machine Learning ◽  
General Strategy ◽  
Weak Supervision ◽  
Validation Experiment ◽  
Mri Sequences ◽  
Weakly Supervised

AbstractBiomedical repositories such as the UK Biobank provide increasing access to prospectively collected cardiac imaging, however these data are unlabeled which creates barriers to their use in supervised machine learning. We develop a weakly supervised deep learning model for classification of aortic valve malformations using up to 4,000 unlabeled cardiac MRI sequences. Instead of requiring highly curated training data, weak supervision relies on noisy heuristics defined by domain experts to programmatically generate large-scale, imperfect training labels. For aortic valve classification, models trained with imperfect labels substantially outperform a supervised model trained on hand-labeled MRIs. In an orthogonal validation experiment using health outcomes data, our model identifies individuals with a 1.8-fold increase in risk of a major adverse cardiac event. This work formalizes a learning baseline for aortic valve classification and outlines a general strategy for using weak supervision to train machine learning models using unlabeled medical images at scale.

scholarly journals Merging Weak and Active Supervision for Semantic Parsing

2020 ◽  
Vol 34 (05) ◽  
pp. 8536-8543
Author(s):  
Ansong Ni ◽  
Pengcheng Yin ◽  
Graham Neubig
Keyword(s):  
Active Learning ◽  
State Of The Art ◽  
Semantic Parsing ◽  
Large Space ◽  
Performance Gap ◽  
Weak Supervision ◽  
Domain Experts ◽  
Supervised Training ◽  
Weakly Supervised ◽  
Learning Heuristics

A semantic parser maps natural language commands (NLs) from the users to executable meaning representations (MRs), which are later executed in certain environment to obtain user-desired results. The fully-supervised training of such parser requires NL/MR pairs, annotated by domain experts, which makes them expensive to collect. However, weakly-supervised semantic parsers are learnt only from pairs of NL and expected execution results, leaving the MRs latent. While weak supervision is cheaper to acquire, learning from this input poses difficulties. It demands that parsers search a large space with a very weak learning signal and it is hard to avoid spurious MRs that achieve the correct answer in the wrong way. These factors lead to a performance gap between parsers trained in weakly- and fully-supervised setting. To bridge this gap, we examine the intersection between weak supervision and active learning, which allows the learner to actively select examples and query for manual annotations as extra supervision to improve the model trained under weak supervision. We study different active learning heuristics for selecting examples to query, and various forms of extra supervision for such queries. We evaluate the effectiveness of our method on two different datasets. Experiments on the WikiSQL show that by annotating only 1.8% of examples, we improve over a state-of-the-art weakly-supervised baseline by 6.4%, achieving an accuracy of 79.0%, which is only 1.3% away from the model trained with full supervision. Experiments on WikiTableQuestions with human annotators show that our method can improve the performance with only 100 active queries, especially for weakly-supervised parsers learnt from a cold start. 1

scholarly journals Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions

2013 ◽  
Vol 1 ◽  
pp. 49-62 ◽  
Author(s):  
Yoav Artzi ◽  
Luke Zettlemoyer
Keyword(s):  
Natural Language ◽  
Supervised Learning ◽  
State Of The Art ◽  
Semantic Parsing ◽  
Weak Supervision ◽  
Instruction Sets ◽  
Strong Signal ◽  
Previous State ◽  
Strong Performance ◽  
Weakly Supervised

The context in which language is used provides a strong signal for learning to recover its meaning. In this paper, we show it can be used within a grounded CCG semantic parsing approach that learns a joint model of meaning and context for interpreting and executing natural language instructions, using various types of weak supervision. The joint nature provides crucial benefits by allowing situated cues, such as the set of visible objects, to directly influence learning. It also enables algorithms that learn while executing instructions, for example by trying to replicate human actions. Experiments on a benchmark navigational dataset demonstrate strong performance under differing forms of supervision, including correctly executing 60% more instruction sets relative to the previous state of the art.

Semantic parsing of speech using grammars learned with weak supervision

2015 ◽  
Author(s):  
Judith Gaspers ◽  
Philipp Cimiano ◽  
Britta Wrede
Keyword(s):  
Semantic Parsing ◽  
Weak Supervision

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

scholarly journals Large-scale Semantic Parsing without Question-Answer Pairs

2014 ◽  
Vol 2 ◽  
pp. 377-392 ◽  
Author(s):  
Siva Reddy ◽  
Mirella Lapata ◽  
Mark Steedman
Keyword(s):  
Natural Language ◽  
Large Scale ◽  
Graph Matching ◽  
State Of The Art ◽  
The State ◽  
Semantic Parsing ◽  
Matching Problem ◽  
Weak Supervision ◽  
Benchmark Datasets

In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs. Our key insight is to represent natural language via semantic graphs whose topology shares many commonalities with Freebase. Given this representation, we conceptualize semantic parsing as a graph matching problem. Our model converts sentences to semantic graphs using CCG and subsequently grounds them to Freebase guided by denotations as a form of weak supervision. Evaluation experiments on a subset of the Free917 and WebQuestions benchmark datasets show our semantic parser improves over the state of the art.

scholarly journals Recursive Machine Translation

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Joshua Evans
Keyword(s):  
Machine Learning ◽  
Machine Translation ◽  
Literary Culture ◽  
Literary Texts ◽  
Literary Value ◽  
Translation Tools ◽  
Methodological Reflection

Machine translation tools such as Google Translate are at best seen as useful approximators, rather than offering any literary potential. In this experiment and short methodological reflection, I use Google Translate to recursively translate Austrian poet Georg Trakl’s celebrated WWI poem, ‘Grodek’, between German and English, until the two versions stabilise. I am attentive to places in which the poem and its renderings are simplified and/or literary value may be lost, but also places in which new or unexpected renderings emerge. This is a preliminary foray, but I propose that the method of recursive machine translation offers a new way to explore the translation of literary texts—a timely proposal, given the increasing applications of computer programmes and machine learning both within the humanities and throughout wider literary culture.

scholarly journals Weak Supervision Enhanced Generative Network for Question Generation

2019 ◽  
Author(s):  
Yutong Wang ◽  
Jiyuan Zheng ◽  
Qijiong Liu ◽  
Zhou Zhao ◽  
Jun Xiao ◽  
...  
Keyword(s):  
Question Answering ◽  
Interaction Mechanism ◽  
Generation System ◽  
Dialogue System ◽  
Question Generation ◽  
Weak Supervision ◽  
Weakly Supervised ◽  
The Given ◽  
Automatic Question Generation

Automatic question generation according to an answer within the given passage is useful for many applications, such as question answering system, dialogue system, etc. Current neural-based methods mostly take two steps which extract several important sentences based on the candidate answer through manual rules or supervised neural networks and then use an encoder-decoder framework to generate questions about these sentences. These approaches still acquire two steps and neglect the semantic relations between the answer and the context of the whole passage which is sometimes necessary for answering the question. To address this problem, we propose the Weakly Supervision Enhanced Generative Network (WeGen) which automatically discovers relevant features of the passage given the answer span in a weakly supervised manner to improve the quality of generated questions. More specifically, we devise a discriminator, Relation Guider, to capture the relations between the passage and the associated answer and then the Multi-Interaction mechanism is deployed to transfer the knowledge dynamically for our question generation system. Experiments show the effectiveness of our method in both automatic evaluations and human evaluations.

scholarly journals Comparative Quality Estimation for Machine Translation Observations on Machine Learning and Features

2017 ◽  
Vol 108 (1) ◽  
pp. 307-318 ◽  
Author(s):  
Eleftherios Avramidis
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Machine Translation ◽  
State Of The Art ◽  
Linear Method ◽  
The State ◽  
Quality Estimation ◽  
Art Methods ◽  
Improved Performance

AbstractA deeper analysis on Comparative Quality Estimation is presented by extending the state-of-the-art methods with adequacy and grammatical features from other Quality Estimation tasks. The previously used linear method, unable to cope with the augmented features, is replaced with a boosting classifier assisted by feature selection. The methods indicated show improved performance for 6 language pairs, when applied on the output from MT systems developed over 7 years. The improved models compete better with reference-aware metrics.Notable conclusions are reached through the examination of the contribution of the features in the models, whereas it is possible to identify common MT errors that are captured by the features. Many grammatical/fluency features have a good contribution, few adequacy features have some contribution, whereas source complexity features are of no use. The importance of many fluency and adequacy features is language-specific.

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