paper stringlengths 24 111 | paper_id stringlengths 10 10 | table_caption stringlengths 3 663 | table_column_names sequencelengths 2 14 | table_content_values sequencelengths 1 49 | text stringlengths 116 2.01k | full_body_text stringlengths 19.3k 104k |
|---|---|---|---|---|---|---|
Neural Belief Tracker: Data-Driven Dialogue State Tracking | 1606.03777 | Table 2: DSTC2 and WOZ 2.0 test set performance (joint goals and requests) of the NBT-CNN model making use of three different word vector collections. The asterisk indicates statistically significant improvement over the baseline xavier (random) word vectors (paired t-test; p<0.05). | [
"[BOLD] Word Vectors",
"[BOLD] DSTC2 [BOLD] Goals",
"[BOLD] DSTC2 [BOLD] Requests",
"[BOLD] WOZ 2.0 [BOLD] Goals",
"[BOLD] WOZ 2.0 [BOLD] Requests"
] | [
[
"xavier [BOLD] (No Info.)",
"64.2",
"81.2",
"81.2",
"90.7"
],
[
"[BOLD] GloVe",
"69.0*",
"96.4*",
"80.1",
"91.4"
],
[
"[BOLD] Paragram-SL999",
"[BOLD] 73.4*",
"[BOLD] 96.5*",
"[BOLD] 84.2*",
"[BOLD] 91.6"
]
] | The NBT models use the semantic relations embedded in the pre-trained word vectors to handle semantic variation and produce high-quality intermediate representations. , trained using co-occurrence information in large textual corpora; and 3) semantically specialised Paragram-SL999 vectors Wieting et al. Paragram-SL999 ... |
\documentclass[11pt,a4paper]{article}
\usepackage[hyperref]{acl2017}
\aclfinalcopy % Uncomment this line for the final submission
\setcounter{dbltopnumber}{8}
\setcounter{topnumber}{2}
\setcounter{bottomnumber}{2}
\setcounter{totalnumber}{4}
\renewcommand{\topfraction}{0.85}
\renewcomma... |
Neural Belief Tracker: Data-Driven Dialogue State Tracking | 1606.03777 | Table 1: DSTC2 and WOZ 2.0 test set accuracies for: a) joint goals; and b) turn-level requests. The asterisk indicates statistically significant improvement over the baseline trackers (paired t-test; p<0.05). | [
"[BOLD] DST Model",
"[BOLD] DSTC2 [BOLD] Goals",
"[BOLD] DSTC2 [BOLD] Requests",
"[BOLD] WOZ 2.0 [BOLD] Goals",
"[BOLD] WOZ 2.0 [BOLD] Requests"
] | [
[
"[BOLD] Delexicalisation-Based Model",
"69.1",
"95.7",
"70.8",
"87.1"
],
[
"[BOLD] Delexicalisation-Based Model + Semantic Dictionary",
"72.9*",
"95.7",
"83.7*",
"87.6"
],
[
"Neural Belief Tracker: NBT-DNN",
"72.6*",
"96.4",
"[BOLD] 84.4*",
"91.2... | The NBT models outperformed the baseline models in terms of both joint goal and request accuracies. For goals, the gains are always statistically significant (paired t-test, p<0.05). Moreover, there was no statistically significant variation between the NBT and the lexicon-supplemented models, showing that the NBT can ... |
\documentclass[11pt,a4paper]{article}
\usepackage[hyperref]{acl2017}
\aclfinalcopy % Uncomment this line for the final submission
\setcounter{dbltopnumber}{8}
\setcounter{topnumber}{2}
\setcounter{bottomnumber}{2}
\setcounter{totalnumber}{4}
\renewcommand{\topfraction}{0.85}
\renewcomma... |
What do Neural Machine Translation Models Learn about Morphology? | 1704.03471 | Table 6: Impact of changing the target language on POS tagging accuracy. Self = German/Czech in rows 1/2 respectively. | [
"SourceTarget",
"English",
"Arabic",
"Self"
] | [
[
"German",
"93.5",
"92.7",
"89.3"
],
[
"Czech",
"75.7",
"75.2",
"71.8"
]
] | We report here results that were omitted from the paper due to the space limit. As noted in the paper, all the results consistently show that i) layer 1 performs better than layers 0 and 2; and ii) char-based representations are better than word-based for learning morphology. | \section{Motivation} \label{sec:motivation}
Translating morphologically-rich languages is especially difficult due to a large vocabulary size and a high level of sparsity. Different solutions have been proposed to deal with this problem, for example factored models in phrase-based MT~\cite{koehn-hoang:2007:EMNLP-CoNL... |
What do Neural Machine Translation Models Learn about Morphology? | 1704.03471 | Table 2: POS accuracy on gold and predicted tags using word-based and character-based representations, as well as corresponding BLEU scores. | [
"[EMPTY]",
"Gold",
"Pred",
"BLEU"
] | [
[
"[EMPTY]",
"Word/Char",
"Word/Char",
"Word/Char"
],
[
"Ar-En",
"80.31/93.66",
"89.62/95.35",
"24.7/28.4"
],
[
"Ar-He",
"78.20/92.48",
"88.33/94.66",
"9.9/10.7"
],
[
"De-En",
"87.68/94.57",
"93.54/94.63",
"29.6/30.4"
],
[
"Fr-En",
... | Char-based models always generate better representations for POS tagging, especially in the case of morphologically-richer languages like Arabic and Czech. We observed a similar pattern in the full morphological tagging task. For example, we obtain morphological tagging accuracy of 65.2/79.66 and 67.66/81.66 using word... | \section{Motivation} \label{sec:motivation}
Translating morphologically-rich languages is especially difficult due to a large vocabulary size and a high level of sparsity. Different solutions have been proposed to deal with this problem, for example factored models in phrase-based MT~\cite{koehn-hoang:2007:EMNLP-CoNL... |
What do Neural Machine Translation Models Learn about Morphology? | 1704.03471 | Table 3: POS tagging accuracy using encoder and decoder representations with/without attention. | [
"Attn",
"POS Accuracy ENC",
"POS Accuracy DEC",
"BLEU Ar-En",
"BLEU En-Ar"
] | [
[
"✓",
"89.62",
"86.71",
"24.69",
"13.37"
],
[
"✗",
"74.10",
"85.54",
"11.88",
"5.04"
]
] | "There is a modest drop in representation quality with the decoder. This drop may be correlated with(...TRUNCATED) | "\\section{Motivation} \\label{sec:motivation}\n\nTranslating morphologically-rich languages is espe(...TRUNCATED) |
What do Neural Machine Translation Models Learn about Morphology? | 1704.03471 | Table 4: POS tagging accuracy using word-based and char-based encoder/decoder representations. | [
"[EMPTY]",
"POS Accuracy ENC",
"POS Accuracy DEC",
"BLEU Ar-En",
"BLEU En-Ar"
] | [
[
"Word",
"89.62",
"86.71",
"24.69",
"13.37"
],
[
"Char",
"95.35",
"91.11",
"28.42",
"13.00"
]
] | "In both bases, char-based representations perform better. BLEU scores behave differently: the char-(...TRUNCATED) | "\\section{Motivation} \\label{sec:motivation}\n\nTranslating morphologically-rich languages is espe(...TRUNCATED) |
What do Neural Machine Translation Models Learn about Morphology? | 1704.03471 | "Table 5: POS and morphology accuracy on predicted tags using word- and char-based representations f(...TRUNCATED) | [
"[EMPTY]",
"Layer 0",
"Layer 1",
"Layer 2"
] | [["[EMPTY]","Word/Char (POS)","Word/Char (POS)","Word/Char (POS)"],["De","91.1/92.0","93.6/95.2","93(...TRUNCATED) | "We report here results that were omitted from the paper due to the space limit. As noted in the pap(...TRUNCATED) | "\\section{Motivation} \\label{sec:motivation}\n\nTranslating morphologically-rich languages is espe(...TRUNCATED) |
What do Neural Machine Translation Models Learn about Morphology? | 1704.03471 | Table 7: POS accuracy and BLEU using decoder representations from different language pairs. | [
"[EMPTY]",
"En-De",
"En-Cz",
"De-En",
"Fr-En"
] | [
[
"POS",
"94.3",
"71.9",
"93.3",
"94.4"
],
[
"BLEU",
"23.4",
"13.9",
"29.6",
"37.8"
]
] | "There is a modest drop in representation quality with the decoder. This drop may be correlated with(...TRUNCATED) | "\\section{Motivation} \\label{sec:motivation}\n\nTranslating morphologically-rich languages is espe(...TRUNCATED) |
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | 1701.06538 | Table 8: Model comparison on 100 Billion Word Google News Dataset | ["Model","Test Perplexity","Test Perplexity","ops/timestep (millions)","#Params excluding embed. & s(...TRUNCATED) | [["[EMPTY]",".1 epochs","1 epoch","[EMPTY]","(millions)","(billions)","(observed)"],["Kneser-Ney 5-g(...TRUNCATED) | ": We evaluate our model using perplexity on a holdout dataset. Perplexity after 100 billion trainin(...TRUNCATED) | "\\documentclass{article} % For LaTeX2e\n\\pdfoutput=1\n\n\n\n\n\n\n\n\n\n\n\n\n\\usepackage[T1]{fon(...TRUNCATED) |
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | 1701.06538 | Table 6: Experiments with different combinations of losses. | ["[ITALIC] wimportance","[ITALIC] wload","Test Perplexity","[ITALIC] CV( [ITALIC] Importance( [ITALI(...TRUNCATED) | [["0.0","0.0","39.8","3.04","3.01","17.80"],["0.2","0.0","[BOLD] 35.6","0.06","0.17","1.47"],["0.0",(...TRUNCATED) | "All the combinations containing at least one the two losses led to very similar model quality, wher(...TRUNCATED) | "\\documentclass{article} % For LaTeX2e\n\\pdfoutput=1\n\n\n\n\n\n\n\n\n\n\n\n\n\\usepackage[T1]{fon(...TRUNCATED) |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 4