e2e_cleaned

Deskripsi :

Rilis pembaruan data E2E NLG Challenge dengan MR yang dibersihkan. Data E2E berisi representasi makna berbasis tindakan dialog (MR) di domain restoran dan hingga 5 referensi dalam bahasa alami, yang perlu diprediksi.

Dokumentasi Tambahan : Jelajahi di Makalah Dengan Kode
Beranda : https://github.com/tuetschek/e2e-cleaning
Kode sumber : tfds.datasets.e2e_cleaned.Builder
Versi :
- 0.1.0 (default): Tidak ada catatan rilis.
Ukuran unduhan : 13.92 MiB
Ukuran dataset : 14.70 MiB
Di-cache otomatis ( dokumentasi ): Ya
Perpecahan :

Membelah	Contoh
`'test'`	4.693
`'train'`	33.525
`'validation'`	4.299

Struktur fitur :

FeaturesDict({
    'input_text': FeaturesDict({
        'table': Sequence({
            'column_header': string,
            'content': string,
            'row_number': int16,
        }),
    }),
    'target_text': string,
})

Dokumentasi fitur :

Fitur	Kelas	Dtype
	fiturDict
Masukkan teks	fiturDict
masukan_teks/tabel	Urutan
input_text/table/column_header	Tensor	rangkaian
input_teks/tabel/konten	Tensor	rangkaian
input_teks/tabel/nomor_baris	Tensor	int16
target_text	Tensor	rangkaian

Kunci yang diawasi (Lihat as_supervised doc ): ('input_text', 'target_text')
Gambar ( tfds.show_examples ): Tidak didukung.
Contoh ( tfds.as_dataframe ):

Kutipan :

@inproceedings{dusek-etal-2019-semantic,
    title = "Semantic Noise Matters for Neural Natural Language Generation",
    author = "Du{\v{s} }ek, Ond{\v{r} }ej  and
      Howcroft, David M.  and
      Rieser, Verena",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8652",
    doi = "10.18653/v1/W19-8652",
    pages = "421--426",
    abstract = "Neural natural language generation (NNLG) systems are known for their pathological outputs, i.e. generating text which is unrelated to the input specification. In this paper, we show the impact of semantic noise on state-of-the-art NNLG models which implement different semantic control mechanisms. We find that cleaned data can improve semantic correctness by up to 97{\%}, while maintaining fluency. We also find that the most common error is omitting information, rather than hallucination.",
}