Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains

unsupervised discovery of human-readable text patterns by contrasting a new corpus against a background corpus

Eyal Shnarch, Leshem Choshen, Guy Moshkowich, Ranit Aharonov, Noam Slonim · Findings of EMNLP 2020 · 2020

In one sentence

GrASP^lite turns a supervised pattern-mining algorithm into an unsupervised one by contrasting the new corpus (foreground) with a background corpus — either general English news or an in-domain split — yielding human-readable rules that reveal a corpus's prominent categories with no labels and no list of categories.

Abstract

Approaching new data can be quite deterrent; you do not know how your categories of interest are realized in it, commonly, there is no labeled data at hand, and the performance of domain adaptation methods is unsatisfactory. Aiming to assist domain experts in their first steps into a new task over a new corpus, we present an unsupervised approach to reveal complex rules which cluster the unexplored corpus by its prominent categories (or facets). These rules are human-readable, thus providing an important ingredient which has become in short supply lately - explainability. Each rule provides an explanation for the commonality of all the texts it clusters together. The experts can then identify which rules best capture texts of their categories of interest, and utilize them to deepen their understanding of these categories. These rules can also bootstrap the process of data labeling by pointing at a subset of the corpus which is enriched with texts demonstrating the target categories. We present an extensive evaluation of the usefulness of these rules in identifying target categories, as well as a user study which assesses their interpretability.

Questions this paper answers

where can I start reading about finding interpretable patterns in a text collection that has no labels yet?
what work frames unsupervised pattern discovery over unlabeled corpora as exploratory data analysis for NLP?
how do I explore an unlabeled text corpus and work out what categories to annotate before writing a codebook?
I have a raw corpus and no annotation scheme yet — is there a method that gives me readable patterns instead of a black-box classifier?
GrASP^lite frames the first encounter with an unlabeled corpus as exploratory data analysis for NLP. Instead of a classifier it delivers human-readable patterns an expert can read, edit and merge to discover what categories the corpus contains.
Holds for: Positioned as of 2020 against domain adaptation and per-dataset characterisation work; the paper does not propose using the rule list directly as a classifier.
GrASP^lite requires no labeled data, no list of target categories and no special hardware, running on a normal laptop. It is applicable to any language for which basic text-processing tools exist.
Holds for: All reported experiments are on English corpora; attribute extraction as run uses an English POS tagger, NER, WordNet and an English sentiment lexicon, so a new language needs equivalents.
can readable text patterns learned without any labels pick out the categories in a corpus better than clustering it?
how do unsupervised linguistic pattern rules compare with bag-of-words clustering and Naive Bayes on target-category detection across corpora?
how do I get precision and F1 on a target category such as SMS spam without labeling any training data?
if I have no labels, should I cluster my documents or mine pattern rules to surface the category I care about?
GrASP^lite, given no labels and no list of categories, ranks first on 14 of the 26 target categories evaluated across 10 datasets. The SIB clustering baseline ranks first on all 4 AG's news categories and 4 ISEAR categories, and no method beats the all-positive prior baseline on ISEAR disgust, Polarity or Essays premise.
Holds for: 10 English datasets and 26 categories; the GrASP^lite number per category is the best over an expert simulation that picks top-k rules by Information Gain on a 100–300-sentence annotated validation set.
On SMS spam, GrASP^lite with an in-domain split reaches 93% precision and 82 F1, against 50 F1 for SIB, 30 for Naive Bayes and 23 for the all-positive prior baseline.
Holds for: SMS spam test set, 13% spam prior; the split uses SIB clustering, and the configuration was chosen on a 100-sentence validation set.
when mining patterns from text, does it matter whether the comparison corpus is generic text or another part of the same collection?
how does the choice of background corpus, general English versus an in-domain split, affect the type and quality of discovered contrastive rules?
what should I use as the contrast set when I mine patterns from a domain corpus like Wikipedia comments or terms of service?
should I contrast my documents against generic news text or split my own corpus in two?
A general-English background leads GrASP^lite to rules built on domain jargon words, such as the ToS rule matching "any" and the HOLJ rule matching "section" and "paragraph". An in-domain split instead yields rules relying on abstract syntax, WordNet generalizations and sentiment attributes.
Holds for: Qualitative inspection of rules for the ToS and HOLJ legal corpora against a 50,000-sentence general-English news background; on HOLJ fact and framing the general-English background fails because those sentences are unusual within the corpus but not against general English.
On Wiki attack, GrASP^lite with an in-domain split reaches 54% precision and 44 F1, more than doubling the 24% precision of the SIB clustering used to make the split. The general-English background version stays at the 21 F1 of the prior baseline.
Holds for: Wiki attack test set, 12% attack prior; the general-English background is 50,000 news sentences, stylistically unlike Wikipedia talk pages. Split favours precision over recall (38% vs 93% for the general-English version).
do people find a matched pattern easier to understand than a list of telling keywords when a classifier explains itself?
in a human preference study, are rule-based pattern explanations preferred to Naive Bayes indicative-word explanations?
how do I tell whether pattern explanations are actually more useful to annotators than keyword explanations?
if I want explanations my annotators will accept, are patterns worth the trouble over highlighted words?
Annotators preferred GrASP^lite rule explanations over Naive Bayes indicative-word explanations 53% of the time, abstained 29% of the time, and preferred Naive Bayes only 18% of the time. The study used 20 SMS spam messages that both models classified correctly.
Holds for: 7 annotators, one outlier excluded; SMS spam only, and local explanations of individual predictions rather than judgments about which model predicts better.
can patterns mined with no labels at all compete with a model trained on hundreds of thousands of labeled sentences from another domain?
do unsupervised pattern rules outrank a supervised domain-adaptation baseline on F1 for argument component detection?
how do I detect claims and premises in essays or student writing without labeled data in my own domain?
I cannot label argument spans in my corpus — is transferring a supervised argument-mining model better than mining rules from scratch?
Both GrASP^lite variants outrank BlendNet on F1 for ASRD argument (56 and 55 vs 40) and Essays major claim (42 and 21 vs 17). BlendNet is a supervised domain-adaptation baseline trained on about 200K labeled news sentences.
Holds for: Two computational-argumentation datasets only; BlendNet predicts an argument if any argument type is detected, and GrASP^lite results are the best per category after the validation-set expert simulation.
if only a hundred or so labeled sentences exist, is fine-tuning a pretrained language model still the better option than unsupervised rules?
how does BERT fine-tuned on a small validation split compare with unsupervised pattern rules, and where does it collapse?
how do I decide between fine-tuning BERT and mining rules when my labeled set is a few hundred sentences?
I have about 200 labeled sentences and a large unlabeled corpus — should I just fine-tune BERT?
BERT fine-tuned on the same small validation set outperforms GrASP^lite on most datasets, reaching 97 F1 on SMS spam versus 82. On ToS unfair clause it fails entirely with 0 F1, after 9 trials without a meaningful classification.
Holds for: BERT numbers are averages of 3 runs fine-tuned on the 100–300-sentence validation set, best model after 5 epochs; BERT is not interpretable and so does not serve the expert-assistance scenario.
can patterns found automatically in essays turn up the same phrases that human annotation guidelines list as cues?
do mined pattern rules recover the premise indicators specified in argumentative essay annotation guidelines, and do they extend beyond them?
how do I check whether automatically discovered rules agree with the discourse cues my annotation manual already names?
could mined patterns help me write or extend the indicator list in my own annotation guidelines?
GrASP^lite rules recover premise indicators listed in the Essays annotation guidelines, such as "for example" and "for instance", and generalize to unlisted ones such as "as a matter of fact". The rules come from a knowledgeable in-domain split taking first halves of sentences as foreground and second halves as background.
Holds for: Essays corpus in the computational-argumentation domain; the sentence-halves heuristic assumes argumentative structure appears sentence-initially, and the comparison against the Stab and Gurevych guidelines is qualitative.
what happens when the category you are hunting for shows up in only a small slice of the documents, like unfair clauses in terms of service?
how does unsupervised contrastive rule mining perform on a low-prior target category in a legal corpus against an all-positive prior baseline?
how do I find a rare category such as unfair contract clauses in a corpus dominated by other content?
my target class is rare in the corpus — should I expect unsupervised pattern mining to find it?
On ToS unfair clause, GrASP^lite reaches only 32 F1 with a general-English background and 25 with an in-domain split, against a 20 F1 prior baseline. The paper attributes this to unfair clauses being a small category next to more prominent ones the rules capture.
Holds for: ToS test set, 11% unfair-clause prior; unsupervised rules describe whatever is prominent in the foreground, so a low-prior target category is not favoured.
can someone reading automatically found patterns name a category that nobody had annotated in the corpus?
has manual inspection of mined rules and their matched sentences yielded an unannotated category in a terms-of-service corpus?
how do I discover categories my dataset's existing label scheme is missing?
if I skim the rules and the sentences they match, will I actually learn something new about my own corpus?
Reading only GrASP^lite rules and their matched sentences for the Terms-of-Service corpus, an author identified an unannotated class of categories, "customer side part in the agreement". It covers what the customer agrees to, may do and must do, while the dataset itself carries only a single "unfair clause" label.
Holds for: One assignee, one dataset of Terms of Service legal documents, self-reported and subjective; no inter-annotator measurement of the discovered categories.
what hardware and resources does it take to mine interpretable patterns from an unlabeled corpus, and does it only work for English?
what are the compute, labeled-data and linguistic-resource requirements of unsupervised contrastive rule mining, and is it language-agnostic?
how do I run pattern discovery on a corpus in a language other than English on a laptop?
can I run this pattern-mining pipeline on my laptop for a non-English corpus?
GrASP^lite requires no labeled data, no list of target categories and no special hardware, running on a normal laptop. It is applicable to any language for which basic text-processing tools exist.
Holds for: All reported experiments are on English corpora; attribute extraction as run uses an English POS tagger, NER, WordNet and an English sentiment lexicon, so a new language needs equivalents.

Claims and scope

Common misreadings

Terminology in this paper

foreground corpus
The new, unexplored corpus of interest, used as the positive set of a pattern-mining algorithm on the assumption that the categories one wants to find are more prominent in it than in a contrasting background corpus.
background corpus
A contrasting set of texts in which the categories of interest are expected to be significantly less prominent, used as the negative set; obtained either by sampling 50,000 general-English news sentences or by splitting the domain corpus itself.
in-domain split
Forming both foreground and background from the same domain corpus — by unsupervised clustering, or by a knowledgeable heuristic such as first sentence halves versus second halves — so that discovered patterns cannot be mere stylistic differences between two domains.
knowledgeable in-domain split
An in-domain split driven by domain intuition rather than clustering; for argumentation, first halves of sentences form the foreground and second halves the background, on the hypothesis that argumentative structure appears sentence-initially.
rule
A pattern of term-level linguistic attributes — surface form, POS tag, named entity, WordNet hypernym or super class, sentiment-lexicon membership — matched within a 5-term window, readable as a sentence such as "an ordinal number followed by a term relating to human communication".
expert simulation
A surrogate for a human filtering rules: for each category, the top k ∈ {100, 50, 25, 10} rules by Information Gain on a small annotated validation set are kept, and a sentence is called positive when at least x ∈ {10, 5, 2, 1} of them match.
prior baseline
Labelling every instance positive, so recall is trivially 100% and precision equals the target category's share of the data; the F1 it yields is the bar an unsupervised method must clear.

How to cite

@inproceedings{DBLP:conf/emnlp/ShnarchCMAS20,author       = {Eyal Shnarch and
                  Leshem Choshen and
                  Guy Moshkowich and
                  Ranit Aharonov and
                  Noam Slonim},
  editor       = {Trevor Cohn and
                  Yulan He and
                  Yang Liu},
  title        = {Unsupervised Expressive Rules Provide Explainability and Assist Human
                  Experts Grasping New Domains},
  booktitle    = {Findings of the Association for Computational Linguistics: {EMNLP}
                  2020, Online Event, 16-20 November 2020},
  series       = {Findings of {ACL}},
  volume       = {{EMNLP} 2020},
  pages        = {2678--2697},
  publisher    = {Association for Computational Linguistics},
  year         = {2020},
  url          = {https://doi.org/10.18653/v1/2020.findings-emnlp.243},
  doi          = {10.18653/V1/2020.FINDINGS-EMNLP.243},
  timestamp    = {Tue, 20 Aug 2024 07:54:42 +0200},
  biburl       = {https://dblp.org/rec/conf/emnlp/ShnarchCMAS20.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

References

See the full reference list in the paper.