Notes

Temporally-Aware Feature Pooling For Action Spotting In Soccer Broadcasts
We already expected this phenomenon according to our initial studies on the nature of backtranslation in the BET approach. Label stickers are your best bet. Traditional methods for quantifying sport performances are limited in their capacity to describe the complex interactions of events that occur within a performance over time. If you're at the point in your life, though, where you have the time and the means to research and seek out the perfect classic car, as well as a safe place to keep it and the knowledge to take care of it, well, that's a different story. After removing the uninferable parts of a sentence, the remainder is often relatively short, in which case we sometimes opt to replace the information that was removed with references to other appropriate information from the game statistics, such as the time of the event or the state of the game. In the evaluation, the output for each unique MR is compared against all references and the maximum score is used, naturally leading to higher scores. After that, we describe the training of the generation model on our ice hockey corpus and use automatic evaluation metrics to compare against existing references. In 먹튀검증 , we are presented with the problem of selecting appropriate events from the full game statistics.

We observe that most reports are concise, referencing on average 5.7 events. In Table 1, we summarize the overall size statistics of the final ice hockey corpus after the statistics and news articles have been automatically paired, and events have been manually aligned with the text. We cannot align full sentences with events as, for instance, Barzilay and Lapata (2005) do, as often information not grounded in the statistics is expressed together with statistics-based facts within the same sentences and clauses. As we deploy our text generation model for manual evaluation, we use a Conditional Random Field (CRF) model to predict which events to mention. 2018) on end-to-end natural language generation in spoken dialogue systems. The task is to produce a natural language description of a restaurant based on a given meaning representation (MR)-an unordered set of attributes and their values. Following the data representation used in E2E NLG Challenge experiments, the input events are represented as a linearized sequence of tokens, where XML-style beginning and end tags are used to separate the different features (see Figure 2). This allows the model to directly copy some of the input tokens to the output when necessary. Given a single event described as a sequence of features and their values, our text generation model is trained to produce the text span aligned with it.

Our generation system is compared to the official shared task baseline system, TGen Dušek and Jurčíček (2016), as well as to the top performing participant system on each score (ST top). On two metrics, BLEU and METEOR, our system outperforms the best shared task participants. Our system outperforms the TGen baseline on 3 out of 5 metrics (BLEU, METEOR and ROUGE-L), which is on par with the official shared task results, where not a single one participant system was able to surpass the baseline on all five metrics. A game is played out between two teams, each made up of 9 players. The decoder has two unidirectional LSTM layers with 500 hidden units. Also, each action class might consider different temporal context for the far distant, which would lead to more confusion for the learnable pooling layers. End result occurs in nearly all news articles as the first event, whereas the goal event is by far the most frequent one, each game mentioning on average 3.1 goals. Figure 1, alignments of E7 and E8: The events are expressed differently depending on type of alignment, where the 2-to-1 aligned text says that the team scored two goals.

Furthermore, we are considering each event as a separate training example, independent of other events in the game. A separate coverage attention vector, a sum of past attention distributions, is maintained to inform the model of its past attention decisions. 2017), where the neural attention mechanism in the encoder-decoder model is adapted to jointly model a probability distribution over words from the known vocabulary, a distribution over words from the input sequence to copy and a probability that controls the copying mechanism. To have more comparable numbers to our ice hockey corpus, where we have only one reference for each input event, we also include scores obtained by comparing each MR to each of its reference descriptions separately as if they were individual data points (Ours single ref.). By contrast, in other text generation datasets such as the E2E NLG Challenge, the output text in general describes all input features. In addition, we also include features that are meant to inform generation without being copied themselves, for example, the type of the event.

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