Describing a Knowledge Base

    INLG, pp. 10-21, 2018.

    Cited by: 12|Bibtex|Views16|Links
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    Keywords:
    slot typehapoel tel avivGated Recurrent Unithealth careinput kbMore(7+)
    Wei bo:
    We propose a new knowledge base reconstruction based evaluation metric which can be used for other knowledge-driven Natural Language Generation tasks such as news image/video captioning

    Abstract:

    We aim to automatically generate natural language descriptions about an input structured knowledge base (KB). We build our generation framework based on a pointer network which can copy facts from the input KB, and add two attention mechanisms: (i) slot-aware attention to capture the association between a slot type and its corresponding s...More

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    Introduction
    • Show and tell, showing an audience something and telling them about it, is a common classroom activity for early elementary school kids.
    • (Cawsey et al, 1997) presents a natural language generation system to convert structured medical records to natural language text descriptions, which enables more effective communication between health care providers and their patients and among health care providers themselves.
    • The authors aim to fill in this knowledge gap by developing a system that can take a KB about an entity as input, and automatically generate a natural language description (Table 2)
    Highlights
    • Show and tell, showing an audience something and telling them about it, is a common classroom activity for early elementary school kids
    • The availability of vast amounts of Linked Open Data (LOD) and Wikipedia derived resources such as DBPedia, WikiData and YAGO encourages pursuing a new direction of knowledge-driven (Whitehead et al, 2018; Lu et al, 2018) or semantically oriented (BouayadAgha et al, 2013) Natural Language Generation (NLG)
    • We develop an effective generator to produce a natural language description about an input knowledge base
    • Our experiments show that two attention mechanisms focusing on slot type and table position advance state-of-the-art on this task, and provide a knowledge base reconstruction F-score up to 73%
    • We propose a new knowledge base reconstruction based evaluation metric which can be used for other knowledge-driven Natural Language Generation tasks such as news image/video captioning
    • We aim to address the remaining challenges as summarized in Section 3.5, and tackle the setting where multiple facts of the same slot type are not presented in temporal order in the input knowledge base
    Methods
    • 3.1 Data

      Using person and animal entities as case studies, the authors create a new dataset based on Wikipedia dump (2018/04/01) and Wikidata (2018/04/12) as follows: (1).
    • Extract Wikipedia pages and Wikidata tables about person and animal entities, and align them according to their unique KB IDs.
    • For each Wikidata table, filter out the slot types of which frequency is less than 3.
    • Index the row numbers for each slot type according to their orders in the Wikidata table.
    • Build a fixed vocabulary for the whole corpus of ground-truth descriptions and label the words with frequency < 5 as OOV
    Results
    • Table 5 shows the performance of various models with standard metrics.
    • The authors can see that the attention mechanisms achieve consistent improvement.
    • The authors conduct paired t-test between the proposed model and all the other baselines on 10 randomly sampled subsets.
    • As shown in Table 6 and Table 7, the KBs reconstructed from models with these two attention mechanisms achieve much higher quality.
    • Figure 3 and Figure 4 visualize the attentions applied to the walk-through example in Table 1
    Conclusion
    • The authors develop an effective generator to produce a natural language description about an input knowledge base.
    • The authors' experiments show that two attention mechanisms focusing on slot type and table position advance state-of-the-art on this task, and provide a KB reconstruction F-score up to 73%.
    • The authors propose a new KB reconstruction based evaluation metric which can be used for other knowledge-driven NLG tasks such as news image/video captioning.
    • The authors plan to extend the framework to cross-lingual cross-media generation, namely to produce a foreign language description or an image/video about the KB
    Summary
    • Introduction:

      Show and tell, showing an audience something and telling them about it, is a common classroom activity for early elementary school kids.
    • (Cawsey et al, 1997) presents a natural language generation system to convert structured medical records to natural language text descriptions, which enables more effective communication between health care providers and their patients and among health care providers themselves.
    • The authors aim to fill in this knowledge gap by developing a system that can take a KB about an entity as input, and automatically generate a natural language description (Table 2)
    • Methods:

      3.1 Data

      Using person and animal entities as case studies, the authors create a new dataset based on Wikipedia dump (2018/04/01) and Wikidata (2018/04/12) as follows: (1).
    • Extract Wikipedia pages and Wikidata tables about person and animal entities, and align them according to their unique KB IDs.
    • For each Wikidata table, filter out the slot types of which frequency is less than 3.
    • Index the row numbers for each slot type according to their orders in the Wikidata table.
    • Build a fixed vocabulary for the whole corpus of ground-truth descriptions and label the words with frequency < 5 as OOV
    • Results:

      Table 5 shows the performance of various models with standard metrics.
    • The authors can see that the attention mechanisms achieve consistent improvement.
    • The authors conduct paired t-test between the proposed model and all the other baselines on 10 randomly sampled subsets.
    • As shown in Table 6 and Table 7, the KBs reconstructed from models with these two attention mechanisms achieve much higher quality.
    • Figure 3 and Figure 4 visualize the attentions applied to the walk-through example in Table 1
    • Conclusion:

      The authors develop an effective generator to produce a natural language description about an input knowledge base.
    • The authors' experiments show that two attention mechanisms focusing on slot type and table position advance state-of-the-art on this task, and provide a KB reconstruction F-score up to 73%.
    • The authors propose a new KB reconstruction based evaluation metric which can be used for other knowledge-driven NLG tasks such as news image/video captioning.
    • The authors plan to extend the framework to cross-lingual cross-media generation, namely to produce a foreign language description or an image/video about the KB
    Tables
    • Table1: Input: Structured Knowledge Base
    • Table2: Human and System Generated Descriptions about the KB in Table 1 plates and styles which human use to describe the same slot type. For example, to describe a football player’s membership with a team, we can use various phrases including member of, traded to, drafted by, played for, face of, loaned to and signed for. Instead of manually crafting patterns for each slot type, we leverage the existing pairs of structured slots from Wikipedia infoboxes and Wikidata (Vrandecicand Krötzsch, 2014) and the corresponding sentences describing these slots in Wikipedia articles as our training data, to learn a deep neural network based generator
    • Table3: Data Statistics
    • Table4: Hyperparameters
    • Table5: Generation Performance based on Standard Metrics %)
    • Table6: Overall Slot Filling Precision (P), Recall (R), F-score (F1) (%)
    • Table7: Inter-dependent Slot Filling Precision (P), Recall (R), F-score (F1) (%)
    Download tables as Excel
    Related work
    Funding
    • This work was supported by the U.S DARPA AIDA Program No FA8750-18-2-0014 and U.S ARL NS-CTA No W911NF-09-2-0053
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