SlowGuess/ACL_2020_OCR huggingface dataset:
In human evaluation, according to the dialogue history and the related knowledge, the annotators evaluate the quality dialog responses in terms of fluency and coherence. The score ranges from 0 to 2; the score is as higher as the responses are more fluent, informative, and coherent to the dialog context and integrate more knowledge. Manual evaluation results are summarized in Table 3, the model achieves high scores both in Wizard-of-Wikipedia and DuConv, meaning that the responses generated by KIC also with good fluency, informativeness,
Models	F1	Bleu-1	Bleu-2	DISTINCT1	DISTINCT2	Parameters
Part1: seq2seq w/o klg.	26.43	0.187	0.100	0.032	0.088	43.47M
Part2: Part1 + w/ klg.	36.59	0.313	0.194	0.071	0.153	43.50M
Part3: Part2 + klg. copy	43.35	0.365	0.249	0.122	0.301	43.59M
KIC: Part3 + dyn. attn.	44.61	0.377	0.262	0.123	0.308	43.63M
Table 4: Automatic Evaluation on progressive components of model KIC over DuConv. Here, klg. and dyn.attn. denote knowledge and dynamic attention, klg.copy stands for knowledge-aware pointer networks. Metrics remain consistent with Table 2.
Models	Test Seen	Test Unseen
Bleu-1/2/3	DISTINCT-1/2	Bleu-1/2/3	DISTINCT-1/2
Part1	0.122/0.049/0.024	0.026/0.07	0.113/0.037/0.014	0.013/0.033
Part2	0.154/0.086/0.060	0.117/0.305	0.140/0.071/0.048	0.038/0.089
Part3	0.165/0.097/0.071	0.129/0.341	0.155/0.088/0.062	0.070/0.168
KIC	0.173/0.105/0.077	0.138/0.363	0.165/0.095/0.068	0.072/0.174
Table 5: Automatic Evaluation on progressive components of model KIC over Wizard-of-Wikipedia. Here, Part1,Part2 and Part3 are the same with Table 4. Metrics remain consistent with Table 1.
and coherence in human view, close to the superior performance of automatic evaluation.
We conduct further ablation experiments to dissect our model. Based on the Seq2Seq framework, we aggrandize it with each key component of model KIC progressively and the results are summarized in Table 4 and Table 5. We first incorporate knowledge into Seq2Seq architecture with dot attention of knowledge and use a gate to control the utilization of knowledge during generation, and the results achieve considerable improvement with the help of knowledge. And then, we apply knowledge-aware pointer networks over the model illustrated in last step to introduce a copy mechanism, which increases effect significantly demonstrates the facilitation of knowledge-aware copy mechanism to produce dialogue response with important words adopted from utterance and knowledge. In the end, we replace the knowledge dot attention by dynamic attention updated with decode state recurrently, which is the whole KIC model proposed in this paper, and the experimental results show that such amelioration also achieves an impressive enhancement. The dynamic update of knowledge attention during decoding effectively integrates multiple knowledge into the response that improves the informativeness. The performances of the model are gradually improved with the addition of components, meaning that each key component of the model KIC plays a crucial role. Additionally, with
the considerable improvement at each progressive step, the model size and the parameters just increase slightly, which means the model KIC has a good cost performance.
3.7 Case Study
As shown in Figure 3, we present the responses generated by our proposed model KIC and the model PostKS(fusion), which achieves overall best performance among competitive baselines. Given utterance and knowledge candidates, our model is better than PostKS(fusion) to produce context-coherence responses incorporating appropriate multiple knowledge with complete descriptions. The model KIC prefers to integrate more knowledge into dialogue response, riching the informative without losing fluency. Furthermore, our model has an additional capability of handling oov problem, which can generate responses with infrequent but important words (which are oov words most of the time) from the knowledge context, like the "Alfred Hitchcock Presents" in Figure 3. We also compare to the result of the model with static knowledge attention, whose result mismatches between the "award" and the representative work "Alfred Hitchcock Presents". The static knowledge attention calculated before decoding, the information and confidence losing with the decoding step by step, leading to mispairing the expression of multiple knowledge. While the recurrent knowledge interaction helps the decoder to fetch the closest knowledge information into the current decoding
Hist.	A: 给你推荐一部口碑不错的爱情电影吧。(Let me recommend you a nice romantic movie.)B: 好啊, 你推荐吧。(Okay, recommend please.)A: 叫迷恋荷尔蒙, 有时间可以带对象一起看看。(Soldier's Girl, you can watch it with your girlfriend when you have time.)B: 不好意思, 我比较喜欢自己看。(Well, I prefer to watch it myself.)A: 其实你是单身吧。(Actually, I guess you are single.)B: 哈哈, 这都让你猜到了, 我都不好意思了。(Aha, you got it, so embarrassed.)A: 嗯嗯。迷恋荷尔蒙这部电影的导演弗兰克·皮尔森可真棒, 把这部电影拍的出神入化。(Uh-huh. The director of this movie Frank Pierson is really awesome, what an amazing film he makes.)B: 这个导演的名字好耳熟啊! (The director's name sounds so familiar!)	Knowledge	弗兰克·皮尔森 Frank Pierson	祖籍 Hometown	美国 America
性别 Gender	男 Male
代表作 Masterpiece	希区柯克悬念故事集 Alfred Hitchcock Presents
职业 Occupation	导演 Director
出生日期 Birth Date	1925-5-12 1925-5-12
迷恋荷尔蒙 Soldier's Girl	导演 Director	弗兰克·皮尔森 Frank Pierson
口碑 Comment	口碑不错的爱情电影 A nice romantic movie
获奖 Award	美国金球奖(2004;第61届)提名-电视类-最佳迷你剧 The Golden Globe Awards (2004; 61st) nominated - TV categories - Best Mini Series
Res.	Target	哈哈。弗兰克·皮尔森导演出生 1925 年 5 月 12 号, 是位美国人哟。Aha. Director Frank Pierson was born on May 12, 1925, and is an American.
PostKS (fusion)	他的代表作是<unk>。His masterpiece is <unk>.
KIC (static)	他是美国人, 还获得过希区柯克悬念故事集。He is an American, and he was awarded Alfred Hitchcock Presents.
KIC (dynamic)	他是 1925 年 5 月 12 日出生的, 他还有一部代表作叫希区柯克悬念故事集。He was born on May 12, 1925. He also has a masterpiece called Alfred Hitchcock Presents.
Figure 3: Case study of DuConv. The <unk> means the out-of-vocabulary. KIC(static) denotes the model using static knowledge attention instead of recurrent knowledge interaction. Knowledge used in responses are in bold letters. Inappropriate words are highlighted with red color.
state, which superior to learn the coherent collocation of multiple knowledge. Some more cases of Wizard-of-Wikipedia and DuConv will present in the appendix section.
Conversation with knowledge incorporation has received considerable interest recently and is demonstrated to be an effective way to enhance performance. There are two main methods in knowledge-based conversation, retrieval-based approaches (Wu et al., 2016; Tian et al., 2019) and generation-based approaches. The generation-based method which achieves more research attention focuses on generating more informative and meaningful responses via incorporate generation with structured knowledge (Zhu et al., 2017; Liu et al., 2018; Young et al., 2018; Zhou et al., 2018) or documental knowledge (Ghazvininejad et al., 2018; Long et al., 2017). Several works integrate knowledge and generation in the pipeline way, which deal with knowledge selection and generation separately. Pipeline approaches pay more attention to knowledge selection, such as using posterior knowledge distribution to facilitate knowledge selection (Lian et al., 2019; Wu et al., 2019b) or used context-aware knowledge pre-selection to guide select knowledge (Zhang et al., 2019). While various works entirety integration the knowledge with generation in an end-to
end way, which usually manage knowledge via external memory module. (Parthasarathi and Pineau, 2018) introduced a bag-of-words memory network and (Dodge et al., 2015) performed dialogue discussion with long-term memory. (Dinan et al., 2018) used a memory network to retrieve knowledge and combined with transformer architectures to generate responses. The pipeline approaches lack of flexibility as constricted by the separated knowledge selection, and the generation could not exploit knowledge sufficiently. The end-to-end approaches with memory module attention to knowledge statically, when integrating multiple knowledge into a response are easier to be confused. Whereas we provide a recurrent knowledge interactive generator that sufficiently fusing the knowledge into generation to produce more informative dialogue responses.
Our work is also inspired by several works of text generation using copy mechanisms. (Vinyals et al., 2015) used attention as a pointer to generate words from the input resource by index-based copy. (Gu et al., 2016) incorporated copying into seq2seq learning to handle unknown words. (See et al., 2017) introduced a hybrid pointer-generator that can copy words from the source text while retaining the ability to produce novel words. In task-oriented dialogue, the pointer networks were also used to improve copy accuracy and mitigate
the common out-of-vocabulary problem (Madotto et al., 2018; Wu et al., 2019a). Different from these works, we extend a pointer network referring to attention distribution of knowledge candidates that can copy words from knowledge resources and generate dialogue responses under the guidance of more complete description from knowledge.
We propose a knowledge grounded conversational model with a recurrent knowledge interactive generator that effectively exploits multiple relevant knowledge to produce appropriate responses. Meanwhile, the knowledge-aware pointer networks we designed allow copying important words, usually oov words, from knowledge. Experimental results demonstrate that our model is powerful to generate much more informative and coherent responses than the competitive baseline models. In future work, we plan to analyze each turn of dialogue with reinforcement learning architecture, and to enhance the diversity of the whole dialogue by avoiding knowledge reuse.
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A Additional Comparison
In dataset Wizard-of-Wikipedia, (Lian et al., 2019) used the metrics Bleu1/2/3, distinct1/2 to evaluate their work, which different from the origin metrics
(PPL, F1) used in (Dinan et al., 2018). In main body, we adopted metrics from (Lian et al., 2019) and compared the baselines presented in their work. We also implements a comparison using PPL&F1 metrics and compare to the methods listed in their paper. The results are summarized in Table 6 and Table 7. The Two-Stage Transformer Memory Networks with knowledge dropout(artificially prevent the model from attending to knowledge a fraction of the time during training) performs best in Test-Seen situation, while our KIC model achieves the best performance at Test-Unseen situation.
Models	Test Seen
PPL	F1
E2E MemNet (no auxiliary loss)	66.5	15.9
E2E MemNet (w/ auxiliary loss)	63.5	16.9
Two-Stage MemNet	54.8	18.6
Two-Stage MemNet (w/ K.D.)	46.5	18.9
KIC	51.9	18.4
Table 6: Comparisons with metrics from (Dinan et al., 2018) over Test-Seen. K.D. denotes knowledge dropout which involves artificial effort.
Models	Test Unseen
PPL	F1
E2E MemNet (no auxiliary loss)	103.6	14.3
E2E MemNet (w/ auxiliary loss)	97.3	14.4
Two-Stage MemNet	88.5	17.4
Two-Stage MemNet (w/ K.D.)	84.8	17.3
KIC	65.8	17.3
Table 7: Comparisons with metrics from (Dinan et al., 2018) over Test-Unseen. K.D. denotes knowledge dropout which involves artificial effort.
B Additional Cases
We have analyzed many cases both on Wizard-of-Wikipedia and DuConv, some of them are presented from Figure 4 to Figure 9. Our model KIC performs well in generating a fluent response coherent to the dialogue history as well as integrating multiple knowledge. Even in no history context situation (the model first to say), the KIC also has the capability of incorporating knowledge to start a knowledge relevant topic.
Hist.	[] Wizard first to say
Knowledge	K1	The domestic cat ("Felis silvestris catus" or "Felis catus") is a small, typically furry, carnivorous mammal.
K2	They are often valued by humans for companionship and for their ability to hunt vermin.
K3	There are more than seventy cat breeds recognized by various cat registries.
K4	Cat senses fit a crepuscular and predatory ecological niche.
Res.	Target	I love cats, I have one names Sam is a small, typically furry, carnivorous mammal.
KIC (ours)	I have a domestic cat that is a small, typically furry, carnivorous mammal
Figure 4: Case of wizard-of-wikipedia with no dialog history.
Hist.	A: "I love cats, I have one names Sam is a small, typically furry, carnivorous mammal."B: "Cats are my favorite animal, I have one too, I think he is a tom since he is a boy",A: "that is neat. They are often called house cats when kept as indoor pets.",B: "Ahh, that totally makes since, I never let my kitty outside, I am terrified of the grossthings they may bring back inside the house!"
Knowledge	K1	They are often called house cats when kept as indoor pets or simply cats when there is no need to distinguish them from other felids and felines.
K2	They are often valued by humans for companionship and for their ability to hunt vermin.
K3	There are more than seventy cat breeds recognized by various cat registries.
K4	Cat senses fit a crepuscular and predatory ecological niche.
Res.	Target	Cats are great pets. They are often valued by humans for companionship
KIC (ours)	They are often valued by humans for companionship and for their ability to hunt vermin.
Figure 5: Case of wizard-of-wikipedia with long knowledge copy.
Hist.	A: "One of my favorite foods is a nice steak with wine"
B: "Which type of steak are you talking about as we have fish steaks, ground meat steaks, pork steak and many more varieties of steaks."
A: "Typically I would go with a meat or pork type steak. I love bacon wrapped pork chops"
Knowledge	K1	Pork is one of the most commonly consumed meats in the world.
K2	In the United States, pork chops are the most commonly consumed meat cut from the pork loin.
K3	Chops are commonly served as an individual portion.
K4	Pork chops are not processed and are leaner than other cuts of pork.
Res.	Target	Definitely. Everyone like pork. It is one of the most commonly consumed meats in the world.
KIC (ours)	I love pork chops which is the most commonly consumed meats cut in the world.
Figure 6: Case of wizard-of-wikipedia with multiple knowledge integration.
Hist.	No History. First to say.	Knowledge	Confessions	类型
Genre	惊悚
Horror
国家
Country	日本
Japan
是否上映
If Released	已上映
Released
领域
Field	电影
Movie
发布日期信息
Release Date	5年前上映
Released 5 years ago
哈纳莱伊湾
Hamatei Bay	国家
Country	发行日期信息
Release Date	今年将会上映
Will be released this year
类型
Genre	剧情
Drama
Res.	Target	喜欢看日本的电影么?
Do you like watching Japanese movies?
KIC (ours)	你喜欢看日本电影吗?
Do you like watching Japanese movies?
Figure 7: Case of DuConv with no dialog history.
Hist.	A: 女性视角是什么样的呢? (What is the female perspective?)B: 这个不清楚。(I don't know.)A: 总要在伤害过后才更明白爱。(Always understand love after the injury.)B: 哐! 是啊。(Ah! Yeah.)A: 有部叫蓝白红三部曲之白,你看一下这个电影,描述的就是女性视角。(A movie called Trois couleurs: Blanc, which describes a female perspective, you can watch it)B: 好的,我会认真观看一下。(Okay, I will watch it carefully.)A: 这个电影是朱丽叶·比诺什的代表作,她跟你一样是双鱼座的哦!(This movie is the masterpiece of Juliet Binoche. She is a Pisces like you!)B: 还挺巧,不过不是很了解。(What a coincidence,but I don't know her very well.)	Knowledge	朱丽叶·比诺什Juliette Binoche	祖籍Hometown	法国巴黎Paris, France
性别Gender	女Female
星座Constellation	双鱼座Pisces
评论Comment	很有母性韵味的女人。。。角色也都收放自如 A woman with a very maternal charm... Her roles are all acted freely.
标签Tag	女性 视角Female perspective
蓝白红三部曲之白Trois couleurs: Blanc	标签Tag	女性 视角Female perspective
口碑Comment	口碑不错Good Reputation
时光网短评MtimeShort Comment	总要在伤害过后才更明白爱。 Always understand love after the injury.
Res.	Target	她的祖籍是在法国巴黎,网友觉得她很有母性韵味,角色的饰演也很到位。Her hometown is Paris, France. Netizens think she has a maternal charm and the roles were all played well.
KIC (ours)	她祖籍是法国巴黎的,网友评论说她是很有母性的女人,角色也都收放自如。Her hometown is Paris, France. Netizens commented that she was a very maternal woman, and the roles were all acted freely.
Figure 8: Case of DuConv with long knowledge copy.
Hist.	A: 2009 获得英国帝国奖最佳女演员提名的知道是谁吗? (Do you know who was nominated for best actress in the British Empire Awards in 2009?)B: 不知道,你知道的话告诉我吧!(I don't know, tell me if you know it!)A: 欧嘉·柯瑞兰寇, 她很迷人, 容貌, 演技还棒。(Olga Kurylenko, she is charming, her looks and acting are also good.)B: 听你说应该很美。(She should be beautiful then.)A: 必须的啊,而且演技还好,主演的一部恐怖片可是吓得我不轻(It must be, and her acting is also good. She starred in a horror movie, which scared me a lot.)B: 是哪部电影啊,都可以吓得到你(Which movie that can scare you?)	Knowledge	欧嘉·柯瑞兰寇Olga Kurylenko	祖籍Hometown	美国America
性别Gender	女Female
评论Comment	她很迷人,容貌,演技She is charming, looks, acting
职业Occupation	演员Actor
主要成就Major Achievements	2009 获得英国帝国奖最佳女演员提名Nominated for Best Actress in the British Empire Awards in 2009
玛拉Mara	导演Director	Clive TongeClive Tonge
上映时间Release Time	2018年2018
类型Genre	恐怖Horror
Res.	Target	是那部2018年上映的玛拉,有时间你可以看一看It's the Mara that was released in 2018. You can take a look at it sometime.
KIC (ours)	名字叫做玛拉,2018年上映的,导演是Clive TongeThe name is Mara and it was released in 2018. The director is Clive Tonge.
Figure 9: Case of DuConv with multiple knowledge integration.