Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Gemini Adv | Llama 3:

Resume:

1.-The goal of semantic parsing is to map natural language to machine-executable language, a challenge since the 1960s.

2.-Examples include querying databases, instructing robots, and asking questions of knowledge bases like Google's Knowledge Graph.

3.-Digital assistants like Alexa, Cortana and Google Home are becoming popular and Bill Gates sees them as an important technological breakthrough.

4.-Three main challenges: structural mismatch between natural and machine language, generating well-formed output, handling different phrasings of the same meaning.

5.-A neural encoder-decoder architecture is used as the backbone - an encoder LSTM represents the input, a decoder LSTM generates the output.

6.-Attention mechanisms allow the decoder to attend to relevant parts of the input representation, improving over just using the final hidden state.

7.-To handle output structure, decoding is modified to generate the output sequentially but hierarchically, using non-terminal tokens to denote hierarchy.

8.-This sequential hierarchical decoding helps but still makes mistakes in parentheses, so a stronger approach is needed to ensure well-formed output.

9.-A two-stage decoding approach first generates an abstract sketch of the output, then fills in the details to get the final output.

10.-The sketch omits low-level details but preserves the core output structure shared by examples with the same basic meaning.

11.-Separating high-level and low-level semantics makes the meaning representation more compact at the sketch level and provides context for the final decoding.

12.-The two-stage model jointly learns to predict sketches and full outputs, maximizing likelihood of meaning representations given natural language inputs.

13.-Templates for sketches are created deterministically by removing variable info, predicate arguments, anonymizing tokens, and collapsing clauses, depending on the meaning representation language.

14.-Two-stage decoding improves accuracy across multiple datasets and meaning representation languages, showing the approach is general.

15.-To handle linguistic variation in how meanings are expressed, paraphrases of the input question are used.

16.-Previous work used paraphrases but generated them separately from question-answering - the two components need to be integrated.

17.-The proposed approach jointly trains a paraphrase scoring model along with the question-answering model for an integrated end-to-end system.

18.-The paraphrase model is crucial - if paraphrases are bad, the whole system fails. Paraphrases are generated via pivoting.

19.-Pivoting translates the input to a foreign language and back to obtain paraphrases. Multiple pivot languages are used for robustness.

20.-A neural machine translation system is used for pivoting, typically based on an encoder-decoder architecture with attention.

21.-Generating paraphrases allows the integrated system to handle questions that are phrased differently from those seen in training.

22.-The integrated paraphrasing and question-answering system outperforms previous approaches on benchmark datasets for semantic parsing.

23.-Key takeaways: encoder-decoder architectures work well for semantic parsing, constrained decoding is important for well-formedness, and paraphrasing improves robustness.

24.-The models are general across datasets and meaning representations. Future work includes testing on more languages/domains and learning from just databases.

25.-Generating paraphrases via GANs is raised as an alternative to pivoting through other languages that could be explored.

26.-Weighting the sketch loss more than the final output loss helps, as predicting good sketches is important.

27.-Fine-tuning large pre-trained language models could help, especially for small datasets, but benefits may diminish for very large training sets.

28.-Using BERT embeddings could potentially improve the similarity scoring between paraphrases and original questions.

29.-Reinforcement learning could be applied when only the final answer, not the full logical form, is available for training.

30.-With only the final answer, many logical forms could be correct, so RL with rewards for well-formedness could guide the search.

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