Dialogue Knowledge Transfer Networks (DiKTNet)

ishalyminov, updated 🕥 2022-06-21 22:45:52

Dialogue Knowledge Transfer Networks (DiKTNet)

This codebase is shared by the following papers: "Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach" by Igor Shalyminov, Sungjin Lee, Arash Eshghi, and Oliver Lemon. [SigDial 2019 publication] [bibtex] [Poster]

"Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks" by Igor Shalyminov, Sungjin Lee, Arash Eshghi, and Oliver Lemon. [EMNLP 2019 publication] [bibtex] [Poster]

If you find it useful for your work, please cite the papers above.

The versions of the code corresponding to each publication can be found using the git release tags.

Pre-requisites

  1. Download the Maluuba MetaLWOz (previously called BLIS) dataset (assuming you extract it into metalwoz-v1 folder)
  2. Prepare environment and dependencies. Below are the steps for Conda:

conda create -n diktnet python=3.6 conda activate diktnet git submodule update --init pip install -r requirements.txt

  1. Create a vocabulary for LAED training: python make_vocabulary.py <data-dir> <corpus client type> <vocab_file.json>

Given the data sources we have, it may be NeuralDialog-ZSDG/data/stanford/ZslStanfordCorpus, NeuralDialog-ZSDG/data/simdial/SimDialCorpus or metalwoz-v1/LAEDBlisCorpus. Vocabularies of a higher coverage can be produced by merging the primary ones.

How To

  1. Training a LAED model - StED (skip-thought dialog-level) python st_ed.py \ metalwoz-v1 \ LAEDBlisCorpus \ vocabs/maluuba.json \ --exlcude_domains ... \ --y_size 10

1.1 Training a LAED model - vanilla VAE (dialog-level) python vae.py \ metalwoz-v1 \ LAEDBlisCorpus \ vocabs/maluuba.json \ --exlcude_domains ... \ --y_size 1 --k 100

  1. Generating LAED features - StED (skip-thought dialog-level) python generate_laed_features.py \ laed_models/st_ed_maluuba_${TARGET_DOMAIN} \ laed_features/st_ed_maluuba__smd_${TARGET_DOMAIN} \ --model_name StED \ --model_type dialog \ --data_dir NeuralDialog-ZSDG/data/stanford \ --corpus_client ZslStanfordCorpus \ --data_loader SMDDialogSkipLoader \ --vocab maluuba.json

  2. Training a Few-Shot Dialog Generation model python train_fsdg.py \ LAZslStanfordCorpus \ --data_dir NeuralDialog-ZSDG/data/stanford \ --laed_z_folders laed_features/st_ed_3x5_maluuba__smd_${domain} \ --black_domains $domain \ --black_ratio 0.9 \ --action_match False \ --target_example_cnt 0 \ --random_seed $rnd

Issues

Bump numpy from 1.15.3 to 1.22.0

opened on 2022-06-21 22:45:51 by dependabot[bot]

Bumps numpy from 1.15.3 to 1.22.0.

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

  • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
  • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
  • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
  • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
  • A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits


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Bump ipython from 5.8.0 to 7.16.3

opened on 2022-01-21 19:54:41 by dependabot[bot]

Bumps ipython from 5.8.0 to 7.16.3.

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Bump pygments from 2.2.0 to 2.7.4

opened on 2021-03-29 20:30:14 by dependabot[bot]

Bumps pygments from 2.2.0 to 2.7.4.

Release notes

Sourced from pygments's releases.

2.7.4

  • Updated lexers:

    • Apache configurations: Improve handling of malformed tags (#1656)

    • CSS: Add support for variables (#1633, #1666)

    • Crystal (#1650, #1670)

    • Coq (#1648)

    • Fortran: Add missing keywords (#1635, #1665)

    • Ini (#1624)

    • JavaScript and variants (#1647 -- missing regex flags, #1651)

    • Markdown (#1623, #1617)

    • Shell

      • Lex trailing whitespace as part of the prompt (#1645)
      • Add missing in keyword (#1652)

    • SQL - Fix keywords (#1668)

    • Typescript: Fix incorrect punctuation handling (#1510, #1511)

  • Fix infinite loop in SML lexer (#1625)

  • Fix backtracking string regexes in JavaScript/TypeScript, Modula2 and many other lexers (#1637)

  • Limit recursion with nesting Ruby heredocs (#1638)

  • Fix a few inefficient regexes for guessing lexers

  • Fix the raw token lexer handling of Unicode (#1616)

  • Revert a private API change in the HTML formatter (#1655) -- please note that private APIs remain subject to change!

  • Fix several exponential/cubic-complexity regexes found by Ben Caller/Doyensec (#1675)

  • Fix incorrect MATLAB example (#1582)

Thanks to Google's OSS-Fuzz project for finding many of these bugs.

2.7.3

... (truncated)

Changelog

Sourced from pygments's changelog.

Version 2.7.4

(released January 12, 2021)

  • Updated lexers:

    • Apache configurations: Improve handling of malformed tags (#1656)

    • CSS: Add support for variables (#1633, #1666)

    • Crystal (#1650, #1670)

    • Coq (#1648)

    • Fortran: Add missing keywords (#1635, #1665)

    • Ini (#1624)

    • JavaScript and variants (#1647 -- missing regex flags, #1651)

    • Markdown (#1623, #1617)

    • Shell

      • Lex trailing whitespace as part of the prompt (#1645)
      • Add missing in keyword (#1652)

    • SQL - Fix keywords (#1668)

    • Typescript: Fix incorrect punctuation handling (#1510, #1511)

  • Fix infinite loop in SML lexer (#1625)

  • Fix backtracking string regexes in JavaScript/TypeScript, Modula2 and many other lexers (#1637)

  • Limit recursion with nesting Ruby heredocs (#1638)

  • Fix a few inefficient regexes for guessing lexers

  • Fix the raw token lexer handling of Unicode (#1616)

  • Revert a private API change in the HTML formatter (#1655) -- please note that private APIs remain subject to change!

  • Fix several exponential/cubic-complexity regexes found by Ben Caller/Doyensec (#1675)

  • Fix incorrect MATLAB example (#1582)

Thanks to Google's OSS-Fuzz project for finding many of these bugs.

Version 2.7.3

(released December 6, 2020)

... (truncated)

Commits
  • 4d555d0 Bump version to 2.7.4.
  • fc3b05d Update CHANGES.
  • ad21935 Revert "Added dracula theme style (#1636)"
  • e411506 Prepare for 2.7.4 release.
  • 275e34d doc: remove Perl 6 ref
  • 2e7e8c4 Fix several exponential/cubic complexity regexes found by Ben Caller/Doyensec
  • eb39c43 xquery: fix pop from empty stack
  • 2738778 fix coding style in test_analyzer_lexer
  • 02e0f09 Added 'ERROR STOP' to fortran.py keywords. (#1665)
  • c83fe48 support added for css variables (#1633)
  • Additional commits viewable in compare view


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Bump pyyaml from 5.1 to 5.4

opened on 2021-03-25 22:56:43 by dependabot[bot]

Bumps pyyaml from 5.1 to 5.4.

Changelog

Sourced from pyyaml's changelog.

5.4 (2021-01-19)

5.3.1 (2020-03-18)

  • yaml/pyyaml#386 -- Prevents arbitrary code execution during python/object/new constructor

5.3 (2020-01-06)

5.2 (2019-12-02)

  • Repair incompatibilities introduced with 5.1. The default Loader was changed, but several methods like add_constructor still used the old default yaml/pyyaml#279 -- A more flexible fix for custom tag constructors yaml/pyyaml#287 -- Change default loader for yaml.add_constructor yaml/pyyaml#305 -- Change default loader for add_implicit_resolver, add_path_resolver
  • Make FullLoader safer by removing python/object/apply from the default FullLoader yaml/pyyaml#347 -- Move constructor for object/apply to UnsafeConstructor
  • Fix bug introduced in 5.1 where quoting went wrong on systems with sys.maxunicode <= 0xffff yaml/pyyaml#276 -- Fix logic for quoting special characters
  • Other PRs: yaml/pyyaml#280 -- Update CHANGES for 5.1

5.1.2 (2019-07-30)

  • Re-release of 5.1 with regenerated Cython sources to build properly for Python 3.8b2+

... (truncated)

Commits
  • 58d0cb7 5.4 release
  • a60f7a1 Fix compatibility with Jython
  • ee98abd Run CI on PR base branch changes
  • ddf2033 constructor.timezone: _copy & deepcopy
  • fc914d5 Avoid repeatedly appending to yaml_implicit_resolvers
  • a001f27 Fix for CVE-2020-14343
  • fe15062 Add 3.9 to appveyor file for completeness sake
  • 1e1c7fb Add a newline character to end of pyproject.toml
  • 0b6b7d6 Start sentences and phrases for capital letters
  • c976915 Shell code improvements
  • Additional commits viewable in compare view


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Bump nltk from 3.2.5 to 3.4.5

opened on 2019-11-02 17:54:45 by dependabot[bot]

Bumps nltk from 3.2.5 to 3.4.5.

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Igor Shalyminov

Applied Scientist at @alexa. PhD from Heriot-Watt University. Past: @microsoft @Maluuba @[email protected] @intel

GitHub Repository

python pytorch machine-learning deep-learning natural-language-processing nlp dialog dialogue-systems dialogue-generation conversational-ai few-shot-learning zero-shot-learning transfer-learning sigdial sigdial2019 emnlp emnlp2019 diktnet