Look at the First Sentence: Position Bias in Question Answering

Many extractive question answering models are trained to predict start and end positions of answers. The choice of predicting answers as positions is mainly due to its simplicity and effectiveness. In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e.g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions can learn spurious positional cues and fail to give answers in different positions. We first illustrate this position bias in popular extractive QA models such as BiDAF and BERT and thoroughly examine how position bias propagates through each layer of BERT. To safely deliver position information without position bias, we train models with various de-biasing methods including entropy regularization and bias ensembling. Among them, we found that using the prior distribution of answer positions as a bias model is very effective at reducing position bias, recovering the performance of BERT from 37.48% to 81.64% when trained on a biased SQuAD dataset.Comment: 13 pages, EMNLP 202

Near-Complete Teleportation of a Superposed Coherent State

The four Bell-type entangled coherent states, |\alpha>|-\alpha> \pm |-\alpha> |\alpha> and |\alpha>|\alpha> \pm |-\alpha> |-\alpha>, can be discriminated with a high probability using only linear optical means, as long as |\alpha| is not too small. Based on this observation, we propose a simple scheme to almost completely teleport a superposed coherent state. The nonunitary transformation, that is required to complete the teleportation, can be achieved by embedding the receiver's field state in a larger Hilbert space consisting of the field and a single atom and performing a unitary transformation on this Hilbert space.Comment: 4 pages,3 figures, Two columns, LaTex2

Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations

Most weakly supervised named entity recognition (NER) models rely on domain-specific dictionaries provided by experts. This approach is infeasible in many domains where dictionaries do not exist. While a phrase retrieval model was used to construct pseudo-dictionaries with entities retrieved from Wikipedia automatically in a recent study, these dictionaries often have limited coverage because the retriever is likely to retrieve popular entities rather than rare ones. In this study, we present a novel framework, HighGEN, that generates NER datasets with high-coverage pseudo-dictionaries. Specifically, we create entity-rich dictionaries with a novel search method, called phrase embedding search, which encourages the retriever to search a space densely populated with various entities. In addition, we use a new verification process based on the embedding distance between candidate entity mentions and entity types to reduce the false-positive noise in weak labels generated by high-coverage dictionaries. We demonstrate that HighGEN outperforms the previous best model by an average F1 score of 4.7 across five NER benchmark datasets.Comment: ACL 202

Customization of IBM Intu’s Voice by Connecting Text-to-Speech Services and a Voice Conversion Network

IBM has recently launched Project Intu, which extends the existing web-based cognitive service Watson with the Internet of Things to provide an intelligent personal assistant service. We propose a voice customization service that allows a user to directly customize the voice of Intu. The method for voice customization is based on IBM Watson’s text-to-speech service and voice conversion model. A user can train the voice conversion model by providing a minimum of approximately 100 speech samples in the preferred voice (target voice). The output voice of Intu (source voice) is then converted into the target voice. Furthermore, the user does not need to offer parallel data for the target voice since the transcriptions of the source speech and target speech are the same. We also suggest methods to maximize the efficiency of voice conversion and determine the proper amount of target speech based on several experiments. When we measured the elapsed time for each process, we observed that feature extraction accounts for 59.7% of voice conversion time, which implies that fixing inefficiencies in feature extraction should be prioritized. We used the mel-cepstral distortion between the target speech and reconstructed speech as an index for conversion accuracy and found that, when the number of target speech samples for training is less than 100, the general performance of the model degrades

Retrieval of NO2 Column Amounts from Ground-Based Hyperspectral Imaging Sensor Measurements

Total column amounts of NO2 (TCN) were estimated from ground-based hyperspectral imaging sensor (HIS) measurements in a polluted urban area (Seoul, Korea) by applying the radiance ratio fitting method with five wavelength pairs from 400 to 460 nm. We quantified the uncertainty of the retrieved TCN based on several factors. The estimated TCN uncertainty was up to 0.09 Dobson unit (DU), equivalent to 2.687 ?? 1020 molecules m???2) given a 1?? error for the observation geometries, including the solar zenith angle, viewing zenith angle, and relative azimuth angle. About 0.1 DU (6.8%) was estimated for an aerosol optical depth (AOD) uncertainty of 0.01. In addition, the uncertainty due to the NO2 vertical profile was 14% to 22%. Compared with the co-located Pandora spectrophotometer measurements, the HIS captured the temporal variation of the TCN during the intensive observation period. The correlation between the TCN from the HIS and Pandora also showed good agreement, with a slight positive bias (bias: 0.6 DU, root mean square error: 0.7 DU)