dataesr/scientific-paragraphs-categorization huggingface dataset:
Section 7. 1 therefore compares BAGEL's performance with O&R's utterance class LM approach, and discusses differences between the two techniques. Data-driven NLG research has also been inspired by research on semantic parsing and machine translation. The WASP -1 generator combines a language model with an inverted synchronous CFG parsing model, effectively casting the generation task as a translation problem from a meaning representation to natural language  (Wong and Mooney 2007). WASP -1 relies on GIZA++ to align utterances with derivations of the meaning representation  (Och and Ney 2003). Although early experiments showed that GIZA++ did not perform well on our data-possibly because of the coarse granularity the-art results on the ROBOCUP dataset and two weather domains. More recently,  Kondadadi, Howald, and Schilder (2013)  also decouple the NLG task as a template extraction and ranking problem, and show that an SVM reranker can produce outputs comparable to human-authored texts, for weather reports and short biographies.
1 onstas and Lapata (2012) jointly model content selection and surface realization by training a forest of PCFGs expressing the relation between records, fields and words. A Viterbi search is used to find the optimal derivations at generation time, however the PCFG weights are rescored using an averaged structured perceptron using both content selection and lexical features. The authors show that their approach outperforms Angeli et al. 's method on the air transport query domain (ATIS dataset). This paper evaluates the same averaged structured perceptron algorithm within the BAGEL framework (see Sections 5. 2 and 7. 2). Most other work on data-driven NLG has focused on learning to map syntax to text. The surface realisation task is an attractive research topic as it is not tied to a specific application domain.
Phrase-based generation from semantic stacks BAGEL uses a stack-based semantic representation to constrain the sequence of semantic concepts to be searched. This representation can be seen as a linearised semantic tree similar to the one previously used for natural language understanding in the Hidden Vector State model  (He and Young 2005). A stack representation provides useful generalisation properties, and it allows for efficient sequential decoding using dynamic programming. In the context of dialogue systems, Figures 1 and 2 illustrate how the input dialogue act-i. e. a semantic tree-is mapped to a set of stacks of semantic concepts (represented as boxes) and aligned with a phrase sequence, resulting in one stack/phrase pair per time frame. The root concept of the semantic tree-i. e.
A major advantage of our formalism compared to more fine-grained formalisms (e. g. , lambda calculus) is that it can be easily understood by human annotators. We believe that this is a crucial point for collecting the range of utterances required for learning to generate natural paraphrases in large domains (see Section 6). Furthermore, Section 8 discusses how its expressiveness could be extended by including additional discourse structures. BAGEL's granularity is defined by the semantic annotation in the training data, rather than external linguistic knowledge about what constitutes a unit of meaning, i. e. contiguous words belonging to the same semantic stack are modelled as an atomic observation unit or phrase. 2 In contrast with word-level language models, a major advantage of phrase-based generation models is that they can model long-range dependencies and domain-specific idiomatic phrases with fewer parameters.
In information presentation systems, such concepts are typically associated with database attributes of the entities of interest. In our framework, the ontology is shared between the dialogue manager, the language understanding component and the NLG component. Our ontology was thus refined over a long period of time prior to this work. The manual effort required for defining an ontology for a new domain is highly dependent on the domain granularity. While automatically deriving ontologies for complex domains remains an unsolved problem, in recent work an ontology for a bus transportation dialogue system was handcrafted in a matter of days  (Thomson et al. 2010). Since there is no feedback between the language generator and the dialogue manager, the NLG component is expected to handle any combination of dialogue act type and semantic concept arguments.
2010). We then ordered the dialogue acts based on their frequency of occurrence in the simulated dialogues. In order to ensure that each semantic stack defined by the domain ontology occurs at least once in our data, we expanded our training set by iteratively adding the most frequent unseen act which contains an unseen mandatory semantic stack. The resulting dataset consists of 1,646 unique dialogue acts after replacing nonenumerable values by a generic symbol. Each dialogue act contains an average of 3. 27 mandatory semantic stacks. We generally collected one utterance per act, however two paraphrases per act were collected during our initial experiment. The resulting dataset contains a total of 1,956 aligned utterances produced by 137 native speakers of English. After manually checking and normalising the dataset, 4  the layered annotations were automatically mapped to phrase-level semantic stacks by splitting the utterance into phrases at annotation boundaries.
Comparison with utterance class language models As Oh and Rudnicky's LM-based approach is the first statistical NLG method that requires almost no handcrafting  (Oh and Rudnicky 2002) , we first compare their method to BAGEL in our domain and discuss the differences between both approaches. At generation time, the LM for the utterance class matching the input is used to overgenerate a set of candidate utterances in a depth-first fashion by sampling from the LM distribution, one word after the other. Since BAGEL relies on the prediction of an end symbol, we extend O&R's model with an end symbol determining when to end the utterance. In addition to random sampling, we also implemented a deterministic version of the algorithm which generates all words that followed the utterance context in the training data, as long as they do not violate input constraints (i.
e. generate unspecified slots). Decoding was halted if the utterance generated more than 20 words. Although it was not needed on our dataset, it is important to note that such a greedy search is likely to require beam pruning on larger datasets. We find that the deterministic version both improves performance and makes it more comparable with BAGEL's decoding algorithm. Additionally, in order to investigate the effect of the granularity of emission symbols on performance, we also train a phrase-based version of the baseline in which the LMs are trained to predict symbols representing contiguous words either within or between surface slots. In all baselines, the final utterance is selected based on an implementation of the rescoring rules used in  (Oh and Rudnicky 2002) , which rescore the utterance based on whether: 1.
For example, Figure  12  shows that the BLEU score decreases significantly from. 28 for trigrams to. 24 and. 20 for 4-grams and 5grams, respectively (p <. 0001 for all differences, no slots in the utterance class). This decrease in performance is likely to be due to overfitting. Larger n-grams are less fertile since they result in fewer non-zero transitions from a given context, hence they are less likely to produce an utterance satisfying the slot constraints. This particular issue could be alleviated by investigating different smoothing strategies. Given the large differences in BLEU score observed and the limited resources available, we did not evaluate O&R's approach using human judges. It is important to note that a human evaluation would be desirable to strengthen our findings. Additionally, future work should evaluate whether the difference in performance holds for larger datasets.
However, this is not tractable as it would require fragmenting the data further to produce all 2 N slot combinations as distinct utterance classes. Sharing data across utterance classes or using hierarchical class-based language models could mitigate this issue, however it is beyond the scope of this paper. This section has shown that BAGEL's FLM approach significantly outperforms utterance class based LMs methods on our data using automated evaluation metrics. We now evaluate BAGEL using human judgements.
For each configuration, we either train all decoding and reranking models on distinct datasets for each dialogue act type in Table  4 , taking the average of each weight update weighted by the number of instances for which the weight vector was left unchanged during training (Avg). In order to account for differences in computational resources needed by each system, we set the pruning thresholds such that each paraphrase set is generated within. 5 seconds on a Pentium 4. 2 GHz. For each input dialogue act, a maximum of 100 realisations were reranked in our experiments. These were derived from up to 5 semantic stack sequences, each generating up to 20 realisation phrase sequences. or For the purpose of the evaluation, the generated paraphrase sets for all systems are combined together and presented in random order, for 4 dialogue acts at a time.
47 Computational Linguistics Just Accepted MS. doi: 10. 1162/COLI_a_00199 © Association for Computational Linguistics We test these hypotheses by conducting a series of observer-based listening tests comparing dialogue extracts in which the system utterances have been regenerated and resynthesized. The original dialogues were collected over the phone during a task-based evaluation of the Hidden Information State dialogue manager  (Young et al. 2010 ) on the CamInfo domain, using a handcrafted rule-based language generator. Each utterance is synthesised using an HMM-based text-to-speech engine trained on the AWB voice of the ARCTIC dataset using the HTS toolkit  (Tokuda et al. 2000). Our evaluation first compares different generation methods in a pairwise fashion: (a) the FLM reranking method with N-best outputs sampled from an 8% selection beam (FLM N-best); (b) the averaged kernelized perceptron reranking method with uniform sampling over positive predictions (Perceptron); and (c) the single output of the handcrafted rule-based generator (Handcrafted).
Discussion and Conclusion This paper presents and evaluates BAGEL, a statistical language generator that can be trained entirely from data, with no handcrafting required beyond the semantic annotation. All the required subtasks-i. e. content ordering, aggregation, lexical selection and realisation-are performed implicitly through a search over Factored Language Models. We propose a stack-based semantic representation at the phrase level, which is expressive enough to generate natural utterances from unseen inputs, yet simple enough for data to be collected from a large set of untrained annotators with minimal manual correction and normalisation. Results show that this approach outperforms utterance class LM methods on our data. In order to make the Viterbi decoding tractable for real world dialogue applications, we limit the context-size of the decoding FLMs and rerank their N-best output using large-context reranking models.
While we believe the granularity of our semantic scheme offers a good balance for dialogue system applications, more research is needed to establish whether more fine-grained semantics can yield a sufficient amount of data in arbitrary domains. inform contrast "#$%&'(!!!!!!/!)!*!!!!!!!!!!!!!!!!!!!!!!/!)!+!!!!!!!!!!!!!!!!!!!!/!)!,!!!!!!!!!!!!!!!!!!!!!!/!)!-!!!!!!!!!!!!!!!!!!!!/!)!. !!!!!!!!!!!!!!!!/!)!/!!!!!!!!!!!!!!!!/!)!0!!!!!!!!!!!!!!/!)!1!!!!!!!!!!!!!!!!!!!!!!!/!)!2!!!!!!!!!!!!!!!!!!!!!!!! The generation of utterances from arbitrary semantic symbols can be difficult for annotators. The BAGEL framework requires the ontology to be designed such that it can be easily annotated. Note that the same requirement exists for collecting data for the system's natural language understanding component.
are smoothed using Witten-Bell interpolated backoff smoothing, according to the backoff graphs in Figure 4. Generally, variables which are the furthest away in time are dropped first, and partial stack variables are dropped last, as they are observed the most. As the optimal backoff strategy can vary depending on the context, the realisation model implements parallel backoff strategies (see steps 2, 4 and 5 in Figure 4(b)), i. e. multiple backoff paths are explored at run-time, and the probability of each backoff node is computed as the maximum probability of all outgoing paths. 4. 2. 3 High cardinality concept abstraction. While one should expect a trainable generator to learn multiple lexical realisations for a given semantic concept, learning lexical realisations for high-cardinality database entries (e.
In this paper we describe a survey to detect USP planets using the entire Kepler dataset. Section 2 describes the data that we utilized in our study. Our Fourier-based transit search technique is explained in Section 3, along with the steps that were used to winnow down thousands of candidates into a final list of 106 likely USP planets. Section 4 presents the properties of the candidates. The issue of false positives within the USP list is examined in Section 5, with the conclusion that the false-positive probability is likely to be low. As a corollary we infer that most USP planets are accompanied by somewhat more distant planets. Section 6 gives estimates for the occurrence rate of USP planets, and its dependence upon period, radius, and the type of host star. Finally, Section 7 provides a summary of our findings and some remarks about the relevance of USP planets within the field of exoplanets.
Kepler data To carry out an independent search for the shortest-period planets, we used the Kepler long-cadence time-series photometric data (30 min samples) obtained between quarters 0 and 16. A list was prepared of all ≈200,000 target stars for which photometry is available for at least one quarter, and the version 5. 0 FITS files, which were available for all quarters, were downloaded from the STScI MAST website. We used the data that had been processed with the PDC-MAP algorithm  (Stumpe et al. 2012; Smith et al. 2012) , which is designed to remove many instrumental artifacts from the time series while preserving any astrophysical variability. We also made use of the time series of the measured X and Y coordinates of the stellar images (the "centroids") in order to test for false positives (see section 3.
In addition, we have found that the standard BLS algorithm produces spurious signals at periods that are integer multiples of the Kepler sampling period of ≈0. 02 day. These spurious peaks constitute a highly significant noise background in searches for planets with periods 0. 5 day. These spurious peaks can be partially suppressed by pre-whitening the data, i. e. , attempting to remove the non-transit astrophysical variability prior to computing the BLS spectrum, but the introduction of such a step complicates the search. A key advantage of the FT method is that FTs can be computed so quickly that it was practical to repeat the search of the entire database several times while the code was being developed (see for example the simulations by  Kondratiev et al. 2009). Although the FT of a transit signal has power that is divided among several harmonics, the number of significant harmonics below the Nyquist limit declines as the orbital period is decreased, and the FT is therefore quite sensitive to the shortest-period planets.
The ratio of transit duration to period, or duty cycle, varies as P -2/3 and is as large as 20% for USP planets, in which case the efficacy of the FT search is nearly equivalent to that of the BLS. Furthermore, it is straightforward to detect a peak in the FT and its equally-spaced harmonics, either by means of an automated algorithm or by eye. The absence of any subharmonics is a useful and important property of true planet transits as opposed to background blended binaries (which often produce subharmonics due to the difference in depth between the primary and secondary eclipses). Regardless of the justification, it is often worthwhile to carry out searches with different techniques. Thus far, the independent searches of the Kepler database have utilized the BLS technique  (Ofir & Dreizler 2013; Huang et al.
At least there are good signs that the completeness is high, based on a comparison between the yield of our pipeline, and the overlap with the various other searches that have been conducted with the Kepler data (see Figure  3 ). We have attempted to measure the completeness of the automated portion of our search by injecting simulated transits into real Kepler light curves and then determining whether they are detected by our pipeline analysis. A similar injectand-recover test for longer-period planets was recently performed by  Petigura et al. (2013). A more realistic simulation in which the signatures of background binaries and other false positive sources, as well as planet transits, are injected into pixel level data, was beyond the scope of our study. To start, we selected a set of 105, 300 Kepler target stars consisting of G-dwarfs and K-dwarfs with m Kep < 16.
SUMMARY AND CONCLUSIONS In this work we have performed a systematic search of the entire Kepler database for ultra-short period (USP) planet candidates, defined as those having orbital periods < 1 day. We utilized a standard Fourier transform algorithm to search for periodic signals in the data, and found it to be quite efficient at finding short-period periodicities. An automated pipeline selected several thousand objects for further investigation including the analysis of transit-profile shapes in the folded light curves. The folded light curves for these candidates were also inspected by eye to yield a first-cut set of 375 interesting candidates. These objects were combined with 127 USP planet candidates from the KOI list, as well as other objects found in the literature, resulting in a set of 471 distinct candidates worthy of detailed study.
STEP-NC a solution for data exchange Designing and manufacturing systems trades (CAD, CAM, Post-Processor, CNC machine, etc. ) are led by specific file. Indeed, software has its own language and makes interoperability more difficult. For example, CATPart and CATProcess will be used respectively for Dassault Systems CAD and CAM systems, Top'CAM for Top-Solid CAM system, G-Code and M-Code will be used as specific inputs according the CNC controller, etc. Therefore to ensure transaction between software, one solution consists in using standard format. Indeed this unifying approach allows having the same language for all the manufacturing technologies. To first achieve the data exchange between CAD and simulation, STEP standard has been developed  (Lee, S. -H. et al. , 2006). The STEP standard is an open and normalized standard that aims to promote the data exchange in a format which is understandable and shared by all.
Information feedback trades As highlighted in the previous sections, STEP-NC standard file encompasses lot of information and permits the data propagation to the whole lifecycle. But STEP-NC does not allow incrementing manufacturing loops. In contrary to the Closed-Loop Machining which concerns a real time correction of machining parameters including the simulation, the Closed-Loop Manufacturing includes the CAM systems, simulation and CNC machines. Moreover this loop doesn't provide a real time feedback but allow incrementing the knowledge from the CNC machine. In fact, the n previous loops would impact and infer on the loop n+1. As STEP-NC file in its own format cannot support the incrementing evolution of good practices. STEP-NC requires to be connected to information systems (PDM-MPM-ERP) to ensure traceability hence to improve the industrialization phase for the future manufactured products.
OntoSTEP-NC for PDM-MPM-ERP trades To achieve interoperability between information systems it has been chosen to use an ontology. In fact it has been chosen to use an ontology called OntoSTEP-NC based on the STEP-NC standard as support PDM-MPM-ERP trades  [15]. The ISO 14649 standard has been chosen as a basis for OntoSTEP-NC model. The standard format is a meta-level model for CNC information so on OntoSTEP-NC describes a meta-level data. This model has been built using the Protégé software (edited by Stanford University)  [16]  defining entities as classes. Figure  2  and figure  3  describe the On-toSTEP-NC entities and its representation. First based on ARM level the OntoSTEP-NC can be then modified to AIM model if necessary. Indeed the ontology lets a large range of possibilities to modify the model structure and to add new entities and features.
Therefore the interoperability of the information systems is achieved thanks to the adaptability of the ontology model to perfectly fit with the data exchange between PDM, MPM and ERP. Moreover, the use of the recent developed OntoSTEP-NC will allow interoperability with CAX systems as explained by  [14] : "Though STEP-NC has given the opportunity for machining process information to be standardized, the lack of a semantic and ontology representation makes it almost impossible to inter-relate the existing systems and languages. " To improve the information feedback, OntoSTEP-NC is being developed as a database in which all the extracted data could be contained. As explained in Lee's work  [17] , the knowledge is not set in a relational database but organized according to the characteristics (class, properties, semantical links, attributes…) of the ontologies.
This organization guaranties the unicity of the information in the database depending the process the feature belongs to. To search information in the ontology, queries will be used and issue queries in query languages such as OWL-QL. This standard language for ontologies query offers the ability to export it to other ontologies if needed for future development by combining many ontologies to OntoSTEP-NC. For example, OntoSTEP-NC could be connected to ONTO-PDM  [18]  which is an ontology for ERP platforms and could also be connected to OntoSTEP  [19]  for the PDM and CAD platforms. Moreover, according to  [20]  semantic queries enable to best capture a user's ontology requirement and rank the resulting ontologies based on their conceptual closeness to the given query. Hence, the information could be spread to all the manufacturing technologies which require this information.
Conclusion As seen in section 2 STEP-NC can support through its rich data format, much information that can be integrated in the bi-directional flow CAD-CAM-CNC. Although STEP-NC allows feedback from the CNC machine to CAD and CAM systems, STEP-NC standard file does not allow archiving and capitalizing information. Indeed, there is no incrementing process in order to capitalize good practices yet existing. This is why the proposition of this paper uses information systems (PDM-MPM-ERP) to ensure this capitalization setting the MPM as a pivot for trades between information systems and CAX technologies. Those trades are led using OntoSTEP-NC and allow having manufacturing loops. This proposition will be tested in our future works on three industrial use cases. Those tests will be extracted from aeronautical parts.
The first part is an aluminum one which presents a large number of manufacturing features (drilling, pocket, surfac-ing…). This first part will test the recognition of geometrical design and the ability to compare to the manufacturing features in the database. The second part which is an aeronautical turning-milling part also in aluminum will test the data extraction due to the numerous complex form of this part. The third part equal to the second one will be in titanium. This one will allow testing the increment of the database and also to test the answers from MPM concerning the relevant CAM information trades. The extrapolation of this proposition will concern complex forms for aeronautical parts with raw material as titanium and Inconel.