Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Human conversations naturally evolve around related entities and connected concepts, while may also shift from topic to topic.
This paper presents ConceptFlow, which leverages commonsense knowledge graphs to explicitly model such conversation flows for better conversation response generation.
ConceptFlow grounds the conversation inputs to the latent concept space and represents the potential conversation flow as a concept flow along the commonsense relations.
The concept is guided by a graph attention mechanism that models the possibility of the conversation evolving towards different concepts.
The conversation response is then decoded using the encodings of both utterance texts and concept flows, integrating the learned conversation structure in the concept space.
Our experiments on Reddit conversations demonstrate the advantage of ConceptFlow over previous commonsense aware dialog models and fine-tuned GPT-2 models, while using much fewer parameters but with explicit modeling of conversation structures.
The rapid advancements of language modeling and natural language generation (NLG) techniques have enabled fully data-driven conversation models, which take user inputs (utterances) and directly generate natural language responses (Shang et al., 2015; Vinyals & Le, 2015; .
On the other hand, the current generation models may still degenerate dull and repetitive contents (Holtzman et al., 2019; Welleck et al., 2019) , which, in conversation assistants, lead to irrelevant, off-topic, and non-useful responses that would damage user experiences (Tang et al., 2019; Zhang et al., 2018; Gao et al., 2019) .
A promising way to address this degeneration challenge is to model conversations with the help of knowledge, for example, open-domain knowledge graph (Ghazvininejad et al., 2018) , commonsense knowledge base (Zhou et al., 2018a) , or background documents (Zhou et al., 2018b) .
Recent research leverages these prior knowledge by grounding the conversation utterances to the external knowledge and integrating them as additional semantic representations; then response can be generated by conditioning on both the text inputs and the grounded semantics (Ghazvininejad et al., 2018; Zhou et al., 2018a; b) .
Integrating external knowledge as a semantic representation of the utterance and an additional input to the conversation model effectively improves the quality of generated responses (Ghazvininejad et al., 2018; Logan et al., 2019; Zhou et al., 2018a) .
On the other hand, human conversations do not stay still on the same set of grounded semantics; instead, our dialog dynamically flows in the semantic space: we shift our discussions from one concept to another, chat about a group of related entities, and may switch dialog topics entirely (Fang et al., 2018) .
Limiting the usages of knowledge only to the grounded ones, effective as they are, does not leverage semantics' full potential in modeling human conversations.
This work presents ConceptFlow, (Conversation generation with Concept Flow), which leverages the commonsense knowledge graph to model the conversation flow in the latent concept space.
Given a conversation utterance, ConceptFlow starts from the grounded knowledge, which in our case are the commonsense concepts appearing in the utterance, and extends to multi-hop concepts along the commonsense relations.
Then the conversation flow is modeled in the extended concept graph using a new fine-grained graph attention mechanism, which learns to encode the concepts using central or outer graph.
Mimicking the development conversation topic flow, the graph attentions guild the concept flow by attending on different directions in the concept flow.
The encoded latent concept flow is integrated to the response generation with standard conditional language models: during decoding, each token, word or concept, is sampled from ConceptFlow's context vector, which combines the encodings of the utterance texts and the latent concept flow.
This enables ConceptFlow to explicitly model the conversation structure when generating responses.
Our experiments on a Reddit conversation dataset (Zhou et al., 2018a ) and a commonsense knowledge graph, ConceptNet (Speer & Havasi, 2012) , demonstrate the advantage of ConceptFlow.
In both automatic and human evaluation, ConceptFlow performs significantly better than various seq2seq based generation models (Sutskever et al., 2014) , as well as previous methods that also leverage commonsense knowledge graph but as static memories (Zhou et al., 2018a; Ghazvininejad et al., 2018; Zhu et al., 2017) .
Notably, ConceptFlow also outperforms two fine-tuned GPT-2 systems (Radford et al., 2019) , despite using much fewer parameters-Effective modeling of conversation structure can reduce the need of large parameter space.
We also provide extensive analyses and case studies to investigate the advantage of modeling conversation flow in the latent concept graph.
Our analyses show that many of Reddit discussions are naturally aligned with the paths in the commonsense knowledge graph; expanding the latent concept graph multiple hops away from the initial grounded concepts significantly improves the coverage on the ground truth response.
Our ablation study further confirms the effectiveness of our graph attention mechanism in selecting useful latent concepts and concepts appearing in golden responses, which help generate more relevant, informative, and less repetitive responses.
In this paper, we present ConceptFlow, which models the conversation flow explicitly as transitions in the latent concept space in order to generate more meaningful responses.
Our experiments on the Reddit conversation dataset illustrate the advantages of ConceptFlow over previous conversational systems that also use prior knowledge, as well as our fine-tuned GPT-2 systems, though the latter uses much more parameters.
Our studies confirm the source of this advantage mainly derive from the high quality and high coverage latent concept flow, which is effectively captured by ConceptFlow's graph attentions.
Our human evaluation demonstrates that ConceptFlow generates more appropriate and informative responses by explicit modeling of the latent conversation structure.
Wenya Zhu, Kaixiang Mo, Yu Zhang, Zhangbin Zhu, Xuezheng Peng, and Qiang Yang.
Flexible end-to-end dialogue system for knowledge grounded conversation.
arXiv preprint arXiv:1709.04264, 2017.
A SUPPLEMENTARY RESULTS Post actually i stayed at the building right next to the lighthouse .
i believe it was a boiler room .
another group was already sleeping at the lighthouse .
CCM i 'm not sure if you 're joking , but i 'm not sure if you 're talking about the lighthouse .
i 'm not sure if it was a blanket or a blanket .
GPT-2 (conv) i 'm pretty sure it was a room with a door .
ConceptFlow good luck , i 'm sure there are some good things in the house .
This paper presents ConceptFlow that explicitly models the conversation flow in commonsense knowledge graph for better conversation generation.