dataesr/scientific-paragraphs-categorization huggingface dataset:
Figure6Twelve-month adherence rates to the five most frequent index antidiabetic drug combinations in PT patients in the JMDC and MDV databases. α-GI, α-glucosidase inhibitor; BG, biguanide; DPP-4i, dipeptidyl peptidase-4 inhibitor; JMDC, Japan Medical Data Center; MDV, Medical Data Vision; PT, previously treated; SU, sulfonylurea.
3) (0. 0) +SGLT2i 256 (9. 0) 128 (11. 6) 11 (3. 0) 2 (0. 6) 7 (6. 9) 2 (4. 0) NA 0 (0. 0) (25. 0) +Insulin 5 (0. 2) 2 (0. 2) 1 (0. 3) 1 (0. 3) 0 (0. 0) 0 (0. 0) 0 (0. 0) NA (0. 0) +GLP-1 0 (0. 0) 16 (1. 5) 2 (0. 5) 2 (0. 6) 0 (0. 0) 0 (0. 0) 0 (0. 0) 0 (0. 0) NA MDV database Index treatment DPP-4i BG SU α-GI TZD Glinide SGLT2i Insulin GLP-1 Pts with add-on therapy n=3179 n=878 n=342 n=344 n=81 n=74 n=35 n=24 n=12 +DPP-4i NA 602 (68. 6) 215 (62. 9) 208 (60. 5) 43 (53. 1) 42 (56. 8) 12 (34. 3) 113 (50. 4) (0. 0) +BG 1168 (36. 7) NA 51 (14. 9) 36 (10. 5) 14 (17. 3) 7 (9. 5) 12 (34. 3) 26 (11. 6) (33. 3) +SU 736 (23. 2) 44 (5. 0) NA 36 (10. 5) 3 (3. 7) 1 (1. 4) 4 (11. 4) 10 (4. 5) (33. 3) +α-GI 414 (13. 0) 38 (4. 3) 29 (8. 5) NA 6 (7. 4) 15 (20. 3) 0 (0. 0) 28 (12. 5) (8. 3) +TZD 168 (5. 3) 29 (3.
0) 11 (3. 3) 0 (0. 0) → SGLT2i 82 (18. 6) 26 (9. 7) 3 (1. 3) 10 (4. 5) 7 (9. 2) 3 (6. 8) NA 2 (0. 6) 1 (16. 7) → Insulin 63 (14. 3) 17 (6. 4) 36 (16. 1) 8 (3. 6) 5 (6. 6) 4 (9. 1) 2 (4. 0) NA 2 (33. 3) → GLP-1 7 (1. 6) 3 (1. 1) 0 (0. 0) 0 (0. 0) 1 (1. 3) 0 (0. 0) 0 (0. 0) 6 (1. 8) NA MDV database Index treatment DPP-4i BG SU α-GI TZD Glinide SGLT2i Insulin GLP-1 Pts with switch therapy n=446 n=271 n=199 n=224 n=69 n=47 n=20 n=417 n=11 → DPP-4i NA 206 (76. 0) 144 (72. 4) 155 (69. 2) 41 (59. 4) 24 (51. 1) 6 (30. 0) 224 (53. 7) 5 (45. 5) → BG 117 (26. 2) NA 15 (7. 5) 21 (9. 4) 14 (20. 3) 4 (8. 5) 6 (30. 0) 34 (8. 2) 2 (18. 2) → SU 51 (11. 4) 12 (4. 4) NA 15 (6. 7) 4 (5. 8) 4 (8. 5) 2 (10. 0) 25 (6. 0) 0 (0. 0) → α-GI 38 (8. 5) 7 (2. 6) 1 (0. 5) NA 1 (1. 4) 7 (14. 9) 0 (0. 0) 26 (6. 2) 0 (0. 0) → TZD 18 (4.
0) 1 (11. 1) 0 (0. 0) +SGLT2i 46 (4. 0) 24 (6. 5) 3 (1. 8) 2 (1. 7) 0 (0. 0) 2 (2. 8) NA 0 (0. 0) 2 (3. 8) +Insulin 8 (0. 7) 3 (0. 8) 0 (0. 0) 1 (0. 8) 0 (0. 0) 0 (0. 0) 0 (0. 0) NA 0 (0. 0) +GLP-1 3 (0. 3) 16 (4. 3) 7 (4. 3) 3 (2. 5) 2 (2. 9) 0 (0. 0) 3 (2. 3) 0 (0. 0) NA MDV database Index treatment DPP-4i BG SU α-GI TZD Glinide SGLT2i Insulin GLP-1 Pts with add- on therapy n=3362 n=616 n=322 n=211 n=53 n=180 n=114 n=335 n=140 +DPP-4i NA 249 (40. 4) 177 (55. 0) 63 (29. 9) 15 (28. 3) 66 (36. 7) 38 (33. 3) 128 (38. 2) 1 (0. 7) +BG 727 (21. 6) NA 31 (9. 6) 8 (3. 8) 7 (13. 2) 16 (8. 9) 23 (20. 2) 35 (10. 4) 18 (12. 9) +SU 768 (22. 8) 38 (6. 2) NA 11 (5. 2) 6 (11. 3) 1 (0. 6) 4 (3. 5) 12 (3. 6) 61 (43. 6) +α-GI 444 (13. 2) 28 (4. 5) 20 (6. 2) NA 1 (1. 9) 25 (13. 9) 3 (2. 6) 28 (8. 4) 11 (7. 9) +TZD 131 (3.
0) → SGLT2i 17 (5. 6) 2 (2. 2) 1 (1. 8) 0 (0. 0) 0 (0. 0) 0 (0. 0) NA 1 (0. 4) 1 (3. 7) → Insulin 106 (35. 0) 37 (40. 2) 18 (32. 1) 37 (52. 9) 6 (13. 0) 6 (15. 8) 9 (18. 0) NA 7 (25. 9) → GLP-1 8 (2. 6) 1 (1. 1) 0 (0. 0) 0 (0. 0) 1 (2. 2) 0 (0. 0) 2 (4. 0) 9 (3. 4) NA MDV database Index treatment DPP-4i BG SU α-GI TZD Glinide SGLT2i Insulin GLP-1 Pts with switch therapy n=651 n=154 n=135 n=119 n=36 n=119 n=34 n=480 n=75 → DPP-4i NA 57 (37. 0) 67 (49. 6) 44 (37. 0) 15 (41. 7) 43 (36. 1) 15 (44. 1) 164 (34. 2) 19 (25. 3) → BG 66 (10. 1) NA 7 (5. 2) 6 (5. 0) 4 (11. 1) 3 (2. 5) 6 (17. 6) 19 (4. 0) 5 (6. 7) → SU 168 (25. 8) 13 (8. 4) NA 6 (5. 0) 4 (11. 1) 22 (18. 5) 1 (2. 9) 48 (10. 0) 4 (5. 3) → α-GI 66 (10. 1) 7 (4. 5) 3 (2. 2) NA 1 (2. 8) 9 (7. 6) 0 (0. 0) 18 (3. 8) 2 (2. 7) → TZD 26 (4. 0) 11 (7.
5 69. 3 58. 1 46. 9 50. 0 43. 0 62. 8 rate (% pts) MDV database n=7658 n=1100 n=633 n=495 n=133 n=446 n=229 Median time to 764. 0 666. 5 532. 0 422. 0 333. 0 396. 0 553. 0 discontinuation (days) 12-month persistence 78. 8 73. 6 62. 2 52. 7 48. 1 52. 2 66. 4 rate (% pts) α-GI, α-glucosidase inhibitor; BG, biguanide; DPP-4i, dipeptidyl peptidase-4 inhibitor; JMDC, Japan Medical Data Center; MDV, Medical Data Vision; PT, previously treated; pts, patients; SGLT2i, sodium-glucose cotransporter-2 inhibitor; SU, sulfonylurea; TZD, thiazolidinedione; UT, untreated.
Determination of relative viscosity (RV) RV of aqueous extracts of white flours and of wholemeal flours of the crosses Yumai 34 x Ukrainka and Yumai 34 x Claire were determined at 30 °C using an automated viscometer (AVS 370, SI Analytics, Germany) fitted with a Micro Ostwald capillary (2 mL, 0. 43 mm) and WinVisco software  (Freeman et al. , 2016). Relative viscosities of wholemeal flours of the crosses Yumai 34 x Altigo and Yumai 34 x Valoris were determined at 30 °C using an automated viscometer (AVS 310, CV (%) 23 22 8 Schott Gerate, Germany) fitted with an Ostwald capillary (2 mL, 0•4 mm)  (Saulnier et al. , 1995).
Acknowledgements Rothamsted Research and the  John Innes Centre  receive grant-aided support from the  Biotechnology and Biological Sciences Research Council (BBSRC)  of the UK and the work reported here forms part of the  Designing Future Wheat Institute Strategic Programme  [ BB/P016855/1 ], with additional support from  Innovate UK  grant,  L005654/1  "High fibre wheat for healthier white bread". We are grateful to  Dr. Jacob Lage  and colleagues at  KWS UK Ltd  for field trials of populations.
Therein, randomly chosen parameters are mutated (randomly modified) and interchanged between individuals, mimicking evolutionary processes in nature and yielding a new population of parametersets. This cyclic process is repeated multiple times (see Fig. 1(a) ) until some stop criterion like a timelimit is met, leading to a close-to-optimum solution to the problem. As EO algorithm we use the implementation of self-adaptive differential evolution ("jDE") in the "pyGMO" toolkit  [41]. It takes as input the directionality of scattering, calculated using "pyGDM"  [42] , our own python implementation of the Green Dyadic Method (GDM)  [43]. By including a surface propagator we can take into account a glass substrate  [44]. Details on our EO-GDM technique can be found elsewhere  [33, 42]. We want to note, that any numerical approach for solving Maxwell's equations can be used together with EO (possible other methods are for example finite difference time domain  [28]  or mode expansion techniques  [32] ).
Funding This work was supported by  Programme Investissements d'Avenir  under the program  ANR-11-IDEX-0002-02 , reference  ANR-10-LABX-0037-NEXT  and by the computing facility center  CALMIP of the University of Toulouse  under grant  P12167. PW received funding by the  German Research Foundation (DFG)  through a  Research Fellowship  ( WI 5261/1-1 ). OM acknowledges support through  EPSRC  grant  EP/M009122/1.
In Natural Language Generation (NLG), Endto-End (E2E) systems trained through deep learning have recently gained a strong interest. Such deep models need a large amount of carefully annotated data to reach satisfactory performance. However, acquiring such datasets for every new NLG application is a tedious and time-consuming task. In this paper, we propose a semi-supervised deep learning scheme that can learn from non-annotated data and annotated data when available. It uses an NLG and a Natural Language Understanding (NLU) sequence-to-sequence models which are learned jointly to compensate for the lack of annotation. Experiments on two benchmark datasets show that, with limited amount of annotated data, the method can achieve very competitive results while not using any preprocessing or re-scoring tricks.
Find The Eagle near Burger King. Figure  1 : Example of Meaning Representation (MR) and one of its paired possible text realizations. This is a excerpt of the E2E NLG challenge dataset. instance, Figure  1  presents, on the upper part, a meaning representation and on the lower part, one possible textual realization to convey this meaning. Although the challenge was a great success, the data used in the challenge contained a lot of redundancy of structure and a limited amount of concepts and several reference texts per MR input (8. 1 in average). This is an ideal case for machine learning but is it the one that is encountered in all E2E NLG real-world applications? In this work, we are interested in learning E2E models for real world applications in which there is a low amount of annotated data.
Indeed, it is well known that neural approaches need a large amount of carefully annotated data to be able to induce NLP models. For the NLG task, that means that MR and (possibly many) reference texts must be paired together so that supervised learning is made possible. In NLG, such paired datasets are rare and remains tedious to acquire  (Novikova et al. , 2017b; Gardent et al. , 2017; Qader et al. , 2018). On the contrary, large amount of unpaired meaning representations and texts can be available but cannot be exploited for supervised learning. In order to tackle this problem, we propose a semi-supervised learning approach which is able to benefit from unpaired (non-annotated) dataset which are much easier to acquire in real life appli-cations. In an unpaired dataset, only the input data is assumed to be representative of the task.
In such case, autoencoders can be used to learn an (often more compact) internal representation of the data. Monolingual word embeddings learning also benefit from unpaired data. However, none of these techniques are fit for the task of generating from a constrained MR representation. Hence, we extend the idea of autoencoder which is to regenerate the input sequence by using an NLG and an NLU models. To learn the NLG model, the input text is fed to the NLU model which in turn feeds the NLG model. The output of the NLG model is compared to the input and a loss can be computed. A similar strategy is applied for NLU. This approach brings several advantages: 1) the learning is performed from a large unpaired (non-annotated) dataset and a small amount of paired data to constrain the inner representation of the models to respect the format of the task (here MR and abstract text); 2) the architecture is completely differentiable which enables a fully joint learning; and 3) the two NLG and NLU models remain independent and can thus be applied to different tasks separately.
E2E NLG challenge Dataset The E2E NLG challenge Dataset has become one of the benchmarks of reference for end-to-end sentence-planning NLG systems. It is still one of the largest dataset available for this task. The dataset was collected via crowd-sourcing using pictorial representations in the domain of restaurant recommendation. Although the E2E challenge dataset contains more than 50k samples, each MR is associated on average with 8. 1 different reference utterances leading to around 6K unique MRs. Each MR consists of 3 to 8 slots, such as name, food or area, and their values and slot types are fairly equally distributed. The majority of MRs consist of 5 or 6 slots while human utterances consist mainly of one or two sentences only. The vocabulary size of the dataset is of 2780 distinct tokens.
The Wikipedia Company Dataset The wikipedia company dataset  (Qader et al. , 2018) , is composed of a set of company data from English Wikipedia. The dataset contains 51k samples where each sample is composed of up to 3 components: the Wikipedia article abstract, the Wikipedia article body, and the infobox which is a set of attribute-value pairs containing primary information about the company (founder, creation date etc. ). The infobox part was taken as MR where each attribute-value pair was represented as a sequence of string attribute  [value]. The MR representation is composed of 41 attributes with 4. 5 attributes per article and 2 words per value in average. The abstract length is between 1 to 5 sentences. The vocabulary size is of 158464 words. The Wikipedia company dataset contains much more lexical variation and semantic information than the E2E challenge dataset.
Furthermore, company texts have been written by humans within the Wikipedia ecosystem and not during a controlled experiment whose human en-gagement was unknown. Hence, the Wikipedia dataset seems an ecological target for research in NLG. However, as pointed out by the authors, the Wikipedia dataset is not ideal for machine learning. First, the data is not controlled and each article contains only one reference (vs. 8. 1 for the E2E challenge dataset). Second the abstract, the body and the infobox are only loosely correlated. Indeed, the meaning representation coverage is poor since, for some MR, none of the information is found in the text and vice-versa. To give a rough estimate of this coverage, we performed an analysis of 100 articles randomly selected in the test set. Over 868 total slot instances, 28% of the slots in the infobox cannot be found in their respective abstract text, while 13% are missing in the infobox.
Experiments The performance of the joint learning architecture was evaluated on the two datasets described in the previous section. The joint learning model requires a paired and an unpaired dataset, so each of the two datasets was split into several parts. E2E NLG challenge Dataset: The training set of the E2E challenge dataset which consists of 42K samples was partitioned into a 10K paired and 32K unpaired datasets by a random process. The unpaired database was composed of two sets, one containing MRs only and the other containing natural texts only. This process resulted in 3 training sets: paired set, unpaired text set and unpaired MR set. The original development set (4. 7K) and test set (4. 7K) of the E2E dataset have been kept. The Wikipedia Company Dataset: The Wikipedia company dataset presented in Section 4.
2 was filtered to contain only companies having abstracts of at least 7 words and at most 105 words. As a result of this process, 43K companies were retained. The dataset was then divided into: a training set (35K), a development set (4. 3K) and a test set (4. 3K). Of course, there was no intersection between these sets. The training set was also partitioned in order to obtain the paired and unpaired datasets. Because of the loose correlation between the MRs and their corresponding text, the paired dataset was selected such that it contained the infobox values with the highest similarity with its reference text. The similarity was computed using "difflib" library 1 , which is an extension of the Ratcliff and Obershelp algorithm  (Ratcliff and Metzener, 1988). The paired set was selected in this way (rather than randomly) to get samples as close as possible to a carefully annotated set.
At the end of partitioning, the following training sets were obtained: paired set (10. 5K), unpaired text set (24. 5K) and unpaired MR set (24. 5K). The way the datasets are split into paired and unpaired sets is artificial and might be biased particularly for the E2E dataset as it is a rather easy dataset. This is why we included the Wikipedia dataset in our study since the possibility of having such bias is low because 1) each company summary/infobox was written by different authors at different time within the wikipedia eco-system making this data far more natural than in the E2E challenge case, 2) there is a large amount of variation in the dataset, and 3) the dataset was split in such a way that the paired set contains perfect matches between the MR and the text, while reserving the least matching samples for the the unpaired set (i.
Evaluation with Automatic Metrics For the experiments, each seq2seq model was composed of 2 layers of Bi-LSTM in the encoder and two layers of LSTM in the decoder with 256 hidden units and dot attention trained using Adam optimization with learning rate of 0. 001. The embeddings had 500 dimensions and the vocabulary was limited to 50K words. The Gumbel-Softmax temperature τ was set to 1. Hyper-parameters tuning was performed on the development set and models were trained until the loss on the development set stops decreasing for several consecutive iterations. All models were implemented with Py-Torch library. Results of the experiment on the E2E challenge data are summarized Table  1  for both the NLG and the NLU tasks. BLEU, Rouge-L and Meteor were computed using the E2E challenge metrics script 2  with default settings.
NLU performances were computed at the slot level. The model learned using paired+unpaired methods shows significant superior performances than the paired version. Among the paired+unpaired methods, the one of last row exhibits the highest balanced score between NLG and NLU. This is achieved when the weights α and γ favor the NLG task against NLU (β = δ = 0. 1). This setting has been chosen since the NLU task converged much quicker than the NLG task. Hence lower weight for NLU during the learning avoided over-fitting. This best system exhibits similar performances than the E2E challenge winner for ROUGE-L and METEOR whereas it did not use any pre-processing (delexicalisation, slot alignment, data augmentation) or re-scoring and was trained on far less annotated data. Results of the experiment on Wikipedia company dataset are summarized Table  2  for both the NLG and the NLU tasks.
Due to noise in the dataset and the fact that only one reference is available for each sample, the automatic metrics show very low scores. This is in line with  (Qader et al. , 2018)  for which the best system obtained BLEU= 0. 0413, ROUGE-L= 0. 266 and METEOR= 0. 1076. Contrary to the previous results, the paired method brings one of the best performance. However, the best performing system is the one of the last row which again put more emphasis on the NLG task than on the NLU one. Once again, this system obtained performances comparable to the best system of  (Qader et al. , 2018)  but without using any pointer generator or coverage mechanisms. In order to further analyze the results, in Table  3  we show samples of the generated text by different models alongside the reference texts. The first two examples are from the model trained on the E2E NLG dataset and the last two are from the Wikipedia dataset.
Although on the E2E dataset the outputs of paired and paired+unpaired models seem very similar, the latter resembles the reference slightly more and because of this it achieves a higher score in the automatic metrics. This resemblance to the reference could be attributed to the fact that we use a reconstruction loss which forces the model to generate text that is only supported by facts found in the input. As for the Wikipedia dataset examples, we can see that the model with paired+unpaired data is less noisy and the outputs are generally shorter. The model with only paired data generates unnecessarily longer text with lots of unsupported facts and repetitions. Needless to say that both models are doing lots of mistakes and this is because of all the noise contained in the training data. the cricketers is a restaurant providing chinese for under 20.
Human Evaluation It is well know that automatic metrics in NLG are poorly predictive of human ratings although they are useful for system analysis and development  (Novikova et al. , 2017a; Gatt and Krahmer, 2018). Hence, to gain more insight about the generation properties of each model, a human evalua-tion with 16 human subjects was performed on the Wikipedia dataset models. We set up a web-based experiment and used the same 4 questions as in  (Qader et al. , 2018)  which were asked on a 5-point Lickert scale: How do you judge the Information Coverage of the company summary? How do you judge the Non-Redundancy of Information in the company summary? How do you judge the Semantic Adequacy of the company summary? How do you judge the Grammatical Correctness of the company summary? For this experiment, 40 company summaries were selected randomly from the test set.
Ablation Study In this section, we further discuss different aspects of the proposed joint learning approach. In particular we are interested in studying the impact of: 1) having different amounts of paired data and 2) the weight of each loss function on the overall performance. Since only the E2E dataset is non-noisy and hence provide meaningful automatic metrics, the ablation study was performed only on this dataset. To evaluate the dependence on the amount of paired data, the best model was re-trained by changing the size of the paired data ranging from 3% of the training data (i. e. , 1K) up to 24% (i. e. , 10K). The results are shown in Figure  3. The figure reveals that regardless of the amount of paired data, the joint learning approach: 1) always improves over the model with only paired data and 2) is always able to benefit from supplementary paired data.
Conclusion and Further Work In this paper, we describe a learning scheme which provides the ability to jointly learn two models for NLG and for NLU using large amount of unannotated data and small amount of annotated data. The results obtained with this method on the E2E challenge benchmark, show that the method can achieve a similar score of the winner of the challenge  (Juraska et al. , 2018)  but with far less annotated data and without using any preprocessing (delexicalisation, data augmentation) or re-scoring tricks. Results on the challenging Wikipedia company dataset shows that highest score can be achieve by mixing paired and unpaired datasets. These results are at the state-ofthe-art level  (Qader et al. , 2018)  but without using any pointer generator or coverage mechanisms. These findings open the way to the exploitation of unannotated data since the lack of large annotated data source is the current bottleneck of E2E NLG systems development for new applications.
is a real estate investment trust and investment trust. it was founded in 1962 by robert c. smith , and is based in new york city , and is headquartered in cleveland , connecticut. the company is headquartered in cleveland , florida , and has offices in new york city , new york , and geneva. Paired+unpaired model the federal realty investment trust , is a real estate investment trust that invests in shopping centers in the united states. it was founded in 1962 by robert duncan , jr. and john epstein. Input name1[ sedgwick group ], headquarters1[ london ], founded1[ 1998 ], in- dustry1[ insurance ], key people1[ sax riley (chairman) ], key people2[ rob whitecooper (ceo) ], defunct1[ 1998 ] Reference sedgwick group plc Input name1[ federal realty investment trust ], founded1[ 1962 ], industry1[ real estate invest- ment trust ], key people1[ joseph s vassalluzzo chairman ], key people2[ donald c wood ceo president ], key people3[ daniel guglielmone cfo ], products1[ shopping centers ], founder1[ samuel j gorlitz ].
, 2001)  but not sufficiently specified. Even if Kd values have been widely reported in the literature, there are still significant uncertainties and gaps about the statistical distributions of freshwater Kd according to the main environmental co-factors. In order to fill these gaps this paper presents conditional statistical Kd distributions for in situ suspended matters as a function of suspended load, dissolve organic carbon and pH. These distributions reduce over several orders of magnitudes the global variability of Kd values, and they should be very helpful to modelers who need to evaluate and reduce the uncertainty of their calculations. They were obtained after an update of the freshwater Kd database presented by Boyer et al. (Boyer et al. , 2018) , implemented by the Working Group 4 (WG4) of the program Modelling and Data for Radiological Impact Assessments (MODARIA I), organised by the International Atomic Energy Agency (IAEA).
Updated database A previous study  (Boyer et al. , 2018)   Finally, the most frequently reported Kd values in freshwaters are those for stable nuclides, suspended sediments and in the case of in situ conditions. Table  S. 1 presents for each element: (i) the type of nuclide (stable or unstable), (ii) the material (suspended or deposited sediment), (iii) the environment (name of river or lake, country), (iv) the source of data values (measurements carried out in situ or in a laboratory) and (v) the co-factors associated to the Kd value (including: m/V ratio, pH, DOC, POC, TOC).