SlowGuess/ACL_2020_OCR huggingface dataset:
Entity Score	Embedding	Overlap (%)	Diversity (%)	Informativeness	R-Score
Metric	Ematch	Euse	Erecall	Emavg	Embex	BLEU-2	BLEU-3	Distinct-1	Distinct-2	Entropy	RaRg
Chinese Weibo
S2S	0.33	0.58	13%	0.770	0.500	2.24	0.80	0.21	1.04	6.09	0.78 0.75
ATS2S	0.33	0.59	12%	0.767	0.513	1.93	0.69	0.27	1.23	5.99	0.77 0.75
ATS2SMMI	0.40	0.74	15%	0.773	0.528	4.01	1.61	0.75	3.91	7.49	1.24 1.21
ATS2SDD1.5	0.35	0.62	13%	0.780	0.542	2.14	0.86	1.03	4.86	7.62	1.16 1.10
Copy	0.33	0.68	13%	0.786	0.501	2.28	0.84	0.59	2.18	6.13	0.92 0.91
GenDS	0.75	0.84	26%	0.789	0.524	2.09	0.73	0.30	1.66	5.89	0.94 0.91
CCM	0.99	1.09	28%	0.786	0.544	3.26	1.20	0.48	2.59	6.16	1.18 1.15
AVG	0.49	0.74	17%	0.779	0.522	2.56	0.96	0.52	2.50	6.48	1.00 1.00
ConKADI	1.48	2.08	38%	0.846	0.577	5.06	1.59	3.26	23.93	9.04	2.98 2.24
ConKADI-cp	1.60	1.89	38%	0.833	0.567	5.00	1.52	2.34	18.29	8.75	2.55 2.08
English Reddit
S2S	0.41	0.52	4%	0.868	0.837	4.81	1.89	0.38	1.77	7.59	0.82 0.78
ATS2S	0.44	0.59	5%	0.863	0.831	4.50	1.81	0.82	3.44	7.62	0.92 0.91
ATS2SMMI	0.45	0.65	6%	0.858	0.825	4.95	2.13	0.75	3.22	7.62	0.95 0.94
ATS2SDD0.3	0.31	0.43	4%	0.830	0.784	1.70	0.75	0.97	3.50	7.47	0.77 0.72
Copy	0.13	0.67	9%	0.868	0.841	5.43	2.26	1.73	8.33	7.87	1.19 1.09
GenDS	1.13	1.26	13%	0.876	0.851	4.68	1.79	0.74	3.97	7.73	1.14 1.10
CCM	1.08	1.33	11%	0.871	0.841	5.18	2.01	1.05	5.29	7.73	1.21 1.18
AVG	0.55	0.77	7%	0.860	0.829	4.40	1.79	0.94	4.32	7.69	1.00 1.00
ConKADI	1.24	1.98	14%	0.867	0.852	3.53	1.27	2.77	18.78	8.50	1.76 1.46
ConKADI-cp	1.41	1.73	13%	0.865	0.855	3.09	1.07	2.29	16.70	8.68	1.63 1.37
Table 2: Objective Experimental Results. The ablation ConKADI  ${}_{-{cp}}$  removes the ability to copy source words.
commonsense knowledge (Zhou et al., 2018).
Implementation: We implemented all models except CCM, CCM was tested based on its official code<sup>7</sup>. Most hyper-parameters are kept the same as CCM, and hyper-parameters among models are kept the same as possible. In detail, the word embedding dimension is 300, Encoder is a 2-layer bidirectional GRU with 512 units, and Decoder is a 2-layer GRU with 512 units. Adam is used to optimizing model with an initial learning rate  $lr = 0.0001$ ; if perplexity begins to increase, the  $lr$  will be halved, if perplexity increases in two continuous epochs, the training will be stopped. Following the CCM, the maximum epoch number is 20.
Objective Metrics: We evaluate the generated responses from four aspects: Knowledge Utilization  $(\mathbf{A_1})$ :  $E_{match}$  is the averaged number of the matched target entities per generation. (Zhou et al., 2018).  $E_{use}$  further counts the source entities.  $E_{recall}$  is the ratio of recalled entities. Embedding-based Relevance  $(\mathbf{A}_{2\mathbf{a}})$ : Following (Liu et al., 2016), we use the  $\mathrm{Emb}_{avg}$  that considers the averaged word embedding, and the  $\mathrm{Emb}_{ex}$  that considers each dimension's extreme value. Overlapping-based Relevance  $(\mathbf{A}_{2\mathbf{b}})$ : BLEU-2/3 (Tian et al., 2017; Wu et al., 2017). Diversity  $(\mathbf{A}_3)$ : We report
the ratio of distinct uni/bi-grams, i.e., Distinct-1/2, in all generated texts (Li et al., 2016a; Wu et al., 2018). Informativeness  $(\mathbf{A}_4)$ : We report the word-level Entropy (Mou et al., 2016).
Relative Score: To illustrate the comprehensive performance of models, we first compute the average score of 7 baselines metric by metric (AVG), then, we report the arithmetic mean score:
$$
R _ {a} = \frac {1}{5} \sum_ {A _ {i}} \left(\frac {1}{\left| A _ {i} \right|} \sum_ {m \in A _ {i}} \frac {m _ {j}}{m _ {j , A V G}}\right) \tag {13}
$$
and the geometric mean score:
$$
R _ {g} = \left(\prod_ {A _ {i}} \left(\prod_ {m _ {j} \in A _ {i}} \frac {m _ {j}}{m _ {j , A V G}}\right) ^ {\frac {1}{| A _ {i} |}}\right) ^ {\frac {1}{5}} \tag {14}
$$
The objective evaluation results on the two datasets have been reported in Table 2. By reviewing the Relative Score, it can be seen that the overall performance of ConKADI outperforms baseline models. More specifically, our ConKADI outperforms baseline models in terms of all metrics except BLEU-3 on the Chinese Weibo, and our ConKADI outperforms baseline models in terms of almost all metrics on the English Reddit. In comparison with the state-of-the-art method CCM, our ConKADI increases the overall performance by  $153\% /95\%$  (arithmetic/geometric mean) on the Chinese dataset, as well as increases the overall performance by  $48\% /25\%$  on the English dataset.
7CCM doesn't support beam-search, so we use the greedy search except  $\mathrm{ATS2S}_{MMI}$  and  $\mathrm{ATS2S}_{DD}$  use beam  $= 10$
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Knowledge Utilization: By accessing the knowledge, three knowledge-aware models, i.e., GenDS, CCM, and ConKADI, can significantly outperform other models. In comparison with GenDS and CCM, the advantages of ConKADI can be summarized as 1) ConKADI has a higher utilization of the knowledge, which can be proved by  $\mathrm{E}_{\text{match}}$ . 2) By using the point-then-copy mechanism (ConKADI vs. ConKADI\_cp), ConKADI further expands the total generated entity number ( $\mathrm{E}_{\text{use}}$ ). After adding the point-then-copy mechanism, while the  $\mathrm{E}_{\text{match}}$  drops by  $7.5\%$ , the overall  $\mathrm{E}_{\text{use}}$  increases by  $10\%$ . It means ConKADI can reasonably decide whether to use a knowledge fact or copy a source word. 3) ConKADI is more potential to find out the accurate knowledge; hence, our  $\mathrm{E}_{\text{recall}}$  is much higher than the  $\mathrm{E}_{\text{recall}}$  of GenDS and CCM. Such results can demonstrate that the proposed Felicitous Fact mechanism can help the model better focus on the facts that are relevant to the dialogue context, and increase the utilization rate of the knowledge graph and the accuracy of the knowledge selection.
Diversity and Informativeness: Generative models have been suffering from generating responses without enough diversity and informativeness. Although previous GenDS and CCM can utilize the knowledge, they fail to solve this challenge; they even can be beaten by other baselines. By contrast, our ConKADI has significantly alleviated this issue. According to our ablation experiments, such notable promotion can be attributed to the proposed Context-Knowledge Fusion. The more detail will be discussed in the ablation study.
Relevance: On the Chinese dataset, ConKADI has the best overall performance, but ConKADI's performance is not ideal on the English dataset. First, we think the reason is the inherent difference of datasets; two datasets are collected from different sources and have varying densities of entityrelations (see Table 1). Next, we must emphasize these metrics can only evaluate the relevance to the given reference. Instead of the 1-to-1 mapping, the dialogue is undoubtedly a 1-to-n mapping; therefore, these results cannot show the generation is not consistent with the query. ConKADI is a very diverse model; only use one reference to judge is unfair. Similarly, this limitation has been found and explained in a recent work (Gao et al., 2019).
4.4 Human Annotation
ConKADI	Appropriateness	Informativeness
vs.	Win	Tie	Lose	Win	Tie	Lose
ATS2S	71.3%	11.0%	17.7 %	87.3%	6.9%	5.8%
ATS2SMMI	59.3%	9.2%	31.5%	82.5%	7.3%	10.2%
Copy	71.7%	8.8%	19.5%	89.7%	3.8%	6.5%
GenDS	87.2%	7.3%	5.5%	93.8%	2.3%	3.5%
CCM	83.8%	6.9%	9.3%	93.0%	3.5%	3.5%
Table 3: Human annotation results on the Chinese Weibo. ConKADI significantly (sign test, p-value  $< 0.005$ , ties are removed) outperforms other baselines in terms of both appropriateness and informativeness.
Following (Liu et al., 2019), we randomly sample 200 query messages from the test set, and then we conduct the pair-wise comparison. For the variations of S2S, We remain two most representative models, ATS2S and  $\mathrm{ATS2S}_{MMI}$ . Thus, we have 1,000 pairs in total. For each pair, we invite three well-educated volunteers to judge which response is better, in terms of the following two metrics: 1) Appropriateness, which mainly considers the fluency and the logical relevance. 2) Informativeness, which considers whether the model provides new information/knowledge or not. The tie is allowed, but volunteers are required to avoid it as possible. The model names are masked, and the A-B order is random.
For the appropriateness, 2/3 agreement (i.e., the percentage of cases that at least 2 volunteers give the same label) is  $95\%$ , and the 3/3 agreement is  $67.1\%$ . For the informativeness, 2/3 agreement is  $97\%$ , and the 3/3 agreement is  $79.1\%$ .
The results have been reported in Table 3.  $\mathrm{ATS2S}_{MMI}$  is the strongest baseline owing to the beam search and the MMI re-ranking, especially in terms of appropriateness. While the generation of  $\mathrm{ATS2S}_{MMI}$  is more generic, it's friendly for human reading; hence, it tends to receive higher scores. GenDS and CCM are far behind our model. We find their generation is usually not fluent, while a lot of entities are generated. Comparing two metrics, ConKADI has more notable advantages in terms of informativeness.
We focus on the ablation of the Felicitous Fact mechanism. There are 3 factors, GlFact (using the distribution  $\mathbf{z}$  to guide the entity word generation), CKF (Context-Knowledge Fusion), and CKF's loss  $\mathcal{L}_f$ . Copy has fully removed the Felicitous Fact mechanism (i.e., above 3 factors); Base further
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Query	#1:My cat likes bananas and bread.	#2:Yeah, but what website?	#3:我会唱霉霉的歌。
I can sing the song of Taylor Swift
ATS2S	I'm a banana and I don't know what you're talking about.	I'm not sure. I'm just curious.	我也是，我唱的是unk。
Me too. I'm singing unk。
ATS2SMMI	Do you have a cat?	It's a site site.	你唱的是哪种歌？
What kind of song are you singing?
Copy	I'm a cat.	I'm not sure what site you're talking about.	我也是，我也是，我也是，我也喜欢。
Me too, me too, me too, I like it。
GenDS	I'm a banana.	I'm not sure, but I'm not sure if it's a link to the original post.
I'm not sure what the site is.	你可以听我唱的唱。
You can listen to my singing singing.
CCM	I'm a banana and I love my cat.	I'm not sure, I just got a link to the site.	我也是，我也喜欢，听着歌着
歌听着歌听着歌
Me too. I like it, too. Listening to songs.
Listening to songs. Listening to songs
ConKADI	And your cat is the best.	Looks like Youtube, the site is blocked.	我听了，他的音乐好听。
I heard it. His music is good.
removes the ability to copy source words.
Table 4: Case Study: #1 #2 are sampled from the English Reddit, #3 is sampled from the Chinese Weibo.
#	Settings	Euse	Distinct-2	Entropy	Rg
#1	Copy+GfFact+CKF+Lf	2.08	23.93	9.04	2.24
#2	Base+GfFact+CKF+Lf	1.89	18.29	8.75	2.02
#3	Copy+GfFact+CKF	1.79	18.18	8.73	2.08
#4	Base+GfFact+CKF	1.92	17.38	8.87	2.01
#5	Base+CKF	1.87	15.72	8.66	1.96
#6	Base+GfFact	1.05	2.90	6.31	1.10
#7	Base	1.06	2.50	6.46	1.10
Table 5: Ablation study on the Chinese Weibo.
The results have been reported in Table 5. 1) The performance drops significantly without using the context-knowledge fused result to initialize the Decoder  $(\# 5\rightarrow \# 7)$ , indicating that CKF is very important for the Decoder. 2) If GlFact is adopted solely, it can affect performance in turn. 3)  $\mathcal{L}_f$  is essential to the Copy in comparison with Base.
Analysis of KL Divergence: The training stage introduces posterior knowledge, which is absent during the inference. Therefore, reducing the difference between such two distributions is very necessary. We here check the curve of the KLD between the  $\mathbf{z}_{\mathrm{prior}}$  and  $\mathbf{z}_{\mathrm{post}}$ , i.e.,  $\mathcal{L}_k$ . A lower  $\mathcal{L}_k$  means the two distributions are closer. As shown in Figure 3: 1) KLD is strongly related to the overall performance. 2) The importance that using the fused knowledge to initialize the Decoder (CKF) has been proved once again (#5 vs. #6).
Three cases are sampled in Table 4. In case 1, except  $\mathrm{ATS2S}_{MMI}$  and our ConKADI, the remaining models have generated weird responses.  $\mathrm{ATS2S}_{MMI}$  generated a fluent response, but this re
Figure 3: The Kullback-Leibler Divergence between the between the  $\mathbf{z}_{\mathrm{prior}}$  and  $\mathbf{z}_{\mathrm{post}}$  on Chinese Weibo against the training iteration number.
sponse is not very logically relevant to the query. In case 2, although GenDS and CCM have generated entity words, they also generate some redundant generic patterns, namely, "I'm not sure ...". It is perhaps because their understanding of background knowledge is still not enough. Our ConKADI generates a fluent and informative response. The last challenging case is sampled from the Chinese dataset. "Taylor Swift" is a female singer, but it is an unknown word for models. All generated responses are not absolutely perfect. Only the generations of ATS2SMMI and ConKADI are fluent. In comparison with ATS2SMMI, the generation of ConKADI provides more information; the only small flaw is ConKADI wrongly thinks "Taylor Swift" is a male singer.
To bridge the gap of the knowledge between machines and human beings in the dialogue genera
tion, this paper proposes a novel knowledge-aware model ConKADI. The proposed Felicitous Fact mechanism can help the ConKADI focus on the facts that are highly relevant to the dialogue context, by generating a felicitous fact probability distribution over the retrieved facts. Besides, the proposed Context-Knowledge Fusion and Flexible Mode Fusion can facilitate the integration of the knowledge in the ConKADI. Extensive evaluations over both an open-released English dataset and our constructed Chinese dataset demonstrate our ConKADI can significantly outperform the state-of-the-art model CCM and other baselines in most experiments.
Although ConKADI has achieved a notable performance, there is still much room to improve. 1) While  $\mathrm{ATS2S}_{MMI}$  is behind our ConKADI, we find MMI can effectively enhance the ATS2S; hence, in the future, we plan to verify the feasibility of the re-ranking technique for knowledge-aware models. 2) We will continue to promote the integration of high-quality knowledge, including more types of knowledge and a more natural integration method.
This work is supported by the National Key R&D Program of China (Grant No. 2017YFB1002000), and PKU-Tencent Joint Innovation Research Program.
Our deepest gratitude goes to the reviewers for their thoughtful suggestions, and we need to thank all our team members in FineLab for their help.
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