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Appendix A. Implementation Details
We use the Adam optimizer (Kingma and Ba, 2015) with learning rate set to 0.0003. The mini-batch size is set to 40 and the weight  $\lambda$  of the copy loss term to 0.7. The dimension of the position embedding is set to 5. For attribute name with multiple words, we average their word embeddings as the attribute name embedding. Refer to our released code and data at https://github.com/czyssrs/Few-Shot-NLG for more details.
Appendix B. ROUGE-4 Results
Following previous work (Liu et al., 2018), we conduct automatic evaluations using BLEU-4 and ROUGE-4 (F-measure) $^3$ . Table 6, 7 and 8 show the ROUGE-4 results for three domains Humans, Books and Songs, respectively.
Domain
# of training instances	Humans
-	50	100	200	500
Template	5.1	-	-	-	-
Base-original	-	0.1	0.4	0.5	0.6
Base	-	0.1	0.4	0.8	1.5
Base+switch	-	4.9	6.3	9.8	12.5
Base+switch+LM-scratch	-	1.0	2.8	4.7	7.1
Base+switch+LM (Ours)	-	14.1	16.2	22.1	28.3
Table 6: ROUGE-4 results on Humans domain
Domain # of training instances	Books
-	50	100	200	500
Template	15.0	-	-	-	-
Base-original	-	1.1	1.6	2.1	1.5
Base	-	1.7	1.5	2.1	2.4
Base+switch	-	12.8	15.0	18.1	20.7
Base+switch+LM-scratch	-	2.4	4.2	6.5	10.7
Base+switch+LM (Ours)	-	22.5	23.1	25.0	27.6
Appendix C. Human Evaluation Details
We conduct human evaluation studies using Amazon Mechanical Turk, based on two aspects: Factual correctness and Language naturalness. For both studies, we evaluate the results trained with 200 training instances of Humans domain. We randomly sample 500 instances from the test set, together with the texts generated with different meth
Table 7: ROUGE-4 results on Books domain
Domain # of training instances	Songs
-	50	100	200	500
Template	24.5	-	-	-	-
Base-original	-	3.4	4.2	4.7	4.8
Base	-	4.1	5.1	4.7	5.8
Base+switch	-	20.2	21.7	23.2	24.8
Base+switch+LM-scratch	-	5.4	8.0	12.0	15.0
Base+switch+LM (Ours)	-	26.2	28.6	30.1	32.6
Table 8: ROUGE-4 results on Songs domain
ods. Each evaluation unit is assigned to 3 workers to eliminate human variance.
The first study attempts to evaluate how well a generated text can correctly convey information in the table. Each worker is present with both the input table and a generated text, and asked to count how many facts in the generated text are supported by the table, and how many are contradicting with or missing from the table, similar as in (Wiseman et al., 2017). The we calculate the average number of supporting and contradicting facts for the texts generated by each method.
The second study aims to evaluate whether the generated text is grammatically correct and fluent in terms of language, regardless of factual correctness. Each worker is present with a pair of texts generated from the same input table, by two different methods, then asked to select the better one only according to language naturalness, or "Tied" if the two texts are of equal quality. The input table is not shown to the workers. Each time a generated text is chosen as the better one, we assign score of 1.0. If two texts are tied, we assign 0.5 for each. We then calculate the average score for the texts generated by each method, indicating its superiority in pairwise comparisons with all other methods.
The significance test is conducted respectively on all three measures: number of supporting facts and number of contradicting facts for the first study; the assigned score for the second study. We use the Tukey HSD post-hoc analysis of an ANOVA with the worker's response as the dependent variable, the method and worker id as independent variables.
3We use standard scripts NIST mteval-v13a.pl (for BLEU), and rouge-1.5.5 (for ROUGE)