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introspection.html
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<!DOCTYPE html><html lang="en-US"><head><meta charset="UTF-8"><meta name="viewport" content="width=device-width,height=device-height,initial-scale=1.0"><meta name="apple-mobile-web-app-capable" content="yes"><meta http-equiv="X-UA-Compatible" content="ie=edge"><meta property="og:type" content="website"><meta name="twitter:card" content="summary"><style>@media screen{body[data-bespoke-view=""] .bespoke-marp-parent>.bespoke-marp-osc>button,body[data-bespoke-view=next] .bespoke-marp-parent>.bespoke-marp-osc>button,body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-info-container button,body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-note-container button{-webkit-tap-highlight-color:transparent;-webkit-appearance:none;-moz-appearance:none;appearance:none;background-color:transparent;border:0;color:inherit;cursor:pointer;font-size:inherit;opacity:.8;outline:none;padding:0;transition:opacity .2s 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slide">Next slide</button><button data-bespoke-marp-osc="fullscreen" tabindex="-1" title="Toggle fullscreen (f)">Toggle fullscreen</button><button data-bespoke-marp-osc="presenter" tabindex="-1" title="Open presenter view (p)">Open presenter view</button></div><div id="p"><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="1">
<h1>Introspection: Examining Pathologies in Neural Models and Bias in Sports Commentary</h1>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="2" data-marpit-fragments="2">
<h1>Outline</h1>
<p>I will introduce two projects that are "introspective."</p>
<ul>
<li data-marpit-fragment="1">Pathologies of Neural Models Make Interpretations Difficult</li>
<li data-marpit-fragment="2">Examining Bias in Sports Commentary with a Large Corpus</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="3">
<ul>
<li>Nueral networks produce strong models for many language tasks.</li>
<li>But are they doing the "right" things?</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="4">
<h1>Pathologies of Neural Models Make Interpretations Difficult</h1>
<p>Shi Feng (University of Marland)<br />
Eric Wallace (University of Maryland)<br />
<strong>Alvin Grissom II (Haverford College)</strong><br />
Mohit Iyyer (University of Maryland)<br />
Pedro Rodriguez (University of Maryland)<br />
Jordan Boy-Graber (University of Maryland)</p>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="5">
<h1>Model Overconfidence</h1>
<ul>
<li>Well-documented adversarial attacks in computer vision (Goodfellow et al., 2014)</li>
</ul>
<img align="right" src="figures/panda_gibbon.png" />
<span style="font-size:75%">
<p>__</p>
<p>Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. "Explaining and harnessing adversarial examples." arXiv preprint arXiv:1412.6572 (2014).</p>
</span>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="6">
<h1>Model Overconfidence</h1>
<ul>
<li>What happens if we remove the <em>unimportant</em> words without changing the prediction? (Feng et al., 2018)<br />
<img align="center" height="420" src="figures/remove_unimportant_1.png" /></li>
</ul>
<p><span style="font-size:50%">__<br />
Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, Jordan Boyd-Graber "Pathologies of neural models make interpretations difficult." EMNLP (2018).<br />
<span></p>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="7">
<h1>Model Overconfidence</h1>
<ul>
<li>What happens if we remove the <em>unimportant</em> words without changing the prediction? (Feng et al., 2018)<br />
<img align="center" height="430" src="figures/remove_unimportant_2.png" /><br />
<span style="font-size:65%">__</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="8">
<p><img src="pathologies/3pathological.PNG" alt="3 pathological cases" /></p>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="9">
<h1>All Examples Drastically Reduced</h1>
<ul>
<li>Run prediction on entire validation set.</li>
<li>Keep prediction the same.</li>
<li>We can consistently reduce examples to very short lengths without changing the prediction.<br />
<img align="center" src="pathologies/pathological_length.PNG" /></li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="10">
<p>Confidence remains high on reduced examples.</p>
<img width=700 align="center" src="pathologies/confidence_high.PNG" />
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="11">
<h1>Humans Confused by Reduced Inputs</h1>
<img width=700 align="center" src="pathologies/humans_confused.PNG" />
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="12">
<h1>How did this happen?</h1>
<img align="center" src="pathologies/already_rubbish.PNG" />
<ul>
<li>After first reduction step, already rubbish, but confidence remains high.</li>
<li>Confidence is <strong>not</strong> the same as uncertainty.</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="13">
<h1>How did this happen?</h1>
<img align="center" src="pathologies/bag_of_words_assumption.PNG" />
<ul>
<li>Implicit bag-of-words assumption.</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="14" data-marpit-fragments="2">
<h1>Mitigation</h1>
<ul>
<li>Can this be mitigated?
<ul>
<li data-marpit-fragment="1">Yes! Modify objective function to co-optimize for high entropy on reduced examples.</li>
<li data-marpit-fragment="2">Ideally, we want a model to say "I don't know" when it doesn't know (uniform distribution of confidences)<br />
<img align="center" src="figures/entropy_objective.png" /></li>
</ul>
</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="15">
<img align="center" src="pathologies/3better.PNG" />
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="16">
<img align="center" width="500" src="pathologies/accuracy_post_reg.PNG" />
<ul>
<li>Accuracy maintained with new model.</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="17">
<img align="center" src="pathologies/accuracy_length_post_reg.PNG" />
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="18">
<img align="center" src="pathologies/reduced_more_meaningful.PNG" />
<ul>
<li>Input reduction leads to more meaningful examples after regularization.</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="19" data-marpit-fragments="2">
<h1>Summary</h1>
<ul>
<li>Neural models are overconfident, making interpretations difficult.
<ul>
<li data-marpit-fragment="1">Poor uncertainty estimates from training.</li>
<li data-marpit-fragment="2">Entropy regularization helps to mitigate.</li>
</ul>
</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="20">
<h1>Investigating Sports Commentator Bias within a Large Corpus of American Football Broadcasts</h1>
<p>Jack Merullo*<br />
Luke Yeh*<br />
Abram Handler*<br />
<strong>Alvin Grissom II+</strong><br />
Brendan O’Connor*<br />
Mohit Iyyer*</p>
<p>*University of Massachussetts--Amherst<br />
+Haverford College</p>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="21">
<h1>Background</h1>
<ul>
<li>Sports commentators inject extemporaneous commentary into on-field sports action.</li>
<li>Prior social science research has examined racially coded language.</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="22">
<h1>Findings from Previous Work</h1>
<ul>
<li>Non-white players are less frequently praised for good play. (Ranville and McCormick, 1977)</li>
<li>White players often credited with "intelligence." (Bruce, 2004; Billings, 2004)</li>
<li>Shortcoming: Previous work used small sample sizes, e.g., Rainville and McCormick (1977) used 16 games and manual coding.</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="23">
<h1>Our Contribution</h1>
<ul>
<li>Revisit earlier work with large-scaled computational analysis.</li>
<li>Collect broadcast American football transcripts from YouTube, identify player mentions, and link mentions to metadata about player's race and position.</li>
<li>Create FOOTBALL dataset</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="24" data-marpit-fragments="1">
<h1>FOOTBALL Dataset</h1>
<ul>
<li>First large-scale sports commentary corpus annotated wth player race.</li>
<li>Over 1,455 NCAA game broadcasts, 2960-2019.</li>
<li>Mentions of players identified and linked to metadata about race and position.</li>
<li>~267,778 player mentions of 4,668 unique players.
<ul>
<li data-marpit-fragment="1">Two confounds
<ol>
<li>racial composition of many play positions is skewed (e.g., 5% of running backsa are white)</li>
<li>Many mentions of players describe only their actions on the field, not their attributes.</li>
</ol>
</li>
</ul>
</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="25">
<h1>Data Processing</h1>
<ul>
<li>Download automatically captioned YouTube videos of games from archival channels (601 NFL games, 854 NCAA games).</li>
<li>Identify teams playing gmae and game's year by exact string matching in video title and manually labeling videos with underspecified titles.</li>
<li>Tokenize with spaCy.</li>
<li>Get part-of-speech tages with ARK TweetNLP (Owoputi et al., 2013).<br />
-Use phrasemachine (Handler et al., 2016) to identify noun phrases.</li>
<li>Link exact string maches of first, last, and full names to roster information in online archives.</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="26" data-marpit-fragments="4">
<h1>Identifying Player Race</h1>
<ul>
<li>We focus on race <strong>perceptions</strong>, as race is a social phenomenon, not a biological one.</li>
<li>We identify each player as white or non-white.</li>
<li>Publicly available rosters do not contain race information, so we crowdsource judgments on race.
<ul>
<li data-marpit-fragment="1">We show crowd workers the player name and a photo from 2,720 images of professional player headshots.</li>
<li data-marpit-fragment="2">5 judgments per photo, for 2,720 images</li>
<li data-marpit-fragment="3">All 5 workers agree on 93% of players.</li>
<li data-marpit-fragment="4">Authors manually examined an additional 1,948 players from Google Image search.</li>
</ul>
</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="27" data-marpit-fragments="1">
<h1>Dataset Analysis</h1>
<ul>
<li>We collect statistics on contextual terms in windows of five tokens around each player mention, following Ananya et al. (2019) for gender mention analysis.</li>
<li>Challenge: many sentences are descriptive, and descriptions can differ by position, which is skewed by race.
<ul>
<li data-marpit-fragment="1">e.g., "Copper is a scrappy receiver", "passes the ball down the field"<br />
<img align="left" height="400" src="sports_bias/fig1.PNG" /><br />
<img align="left" height="400" src="sports_bias/fig2.PNG" /></li>
</ul>
</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="28" data-marpit-fragments="2">
<h1>Naming and Sentiment Patterns</h1>
<ul>
<li>We sidestep confounds in our experiments on <strong>naming patterns</strong> and <strong>sentiment patterns</strong>.</li>
<li><strong>Naming patterns</strong> are how sports commentators refer to players by name (e.g., first or last name).
<ul>
<li data-marpit-fragment="1">Women are more often referred to by their first names in several sports (Koivula, 1999).</li>
<li data-marpit-fragment="2">White players more often referred to as last names in basketball games (Bruce, 2004).</li>
</ul>
</li>
<li>Non-white players are praised more for physical attributes than mental ones (Rada, 1996).</li>
<li><strong>Sentiment patterns</strong> examine positive or negative sentiment about a player.</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="29">
<h1>Naming Patterns</h1>
<p><img align="left" width="500" src="sports_bias/table3.PNG" /> We corroborate previous studies suggesting white players are more often referred to by last names for several positions.</p>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="30" data-marpit-fragments="2">
<h1>Sentiment Patterns</h1>
<ul>
<li>We use domain-specific sentiment lexicon (Hamilton et al., 2006)
<ul>
<li data-marpit-fragment="1">Search within a context window around player mention for words in lexicon.</li>
<li data-marpit-fragment="2">We find 49,787 windows containing sentiment words, 12.8% of which are negative, which is similar to the 8.3% found by Rada (1996).</li>
</ul>
</li>
<li>Rank most positive words by using ratio of relative frequencies.</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="31">
<h1>Sentiment Patterns</h1>
<p><img align="left" width="500" src="sports_bias/table4.PNG" /> Qualitative inspection of list comports with previous research suggesting that nonwhite players more often praised for physical attributes while white players praised for personality and intelligence.</p>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="32">
<h1>Limitations</h1>
<ul>
<li>Small lexicon of results.</li>
<li>Some false positives, e.g., "beast mode" is a nickname for a specific player.</li>
</ul>
<h1>Key Conclusions</h1>
<ul>
<li>First such study on this scale.</li>
<li>Longitudinal analysis of racialized terms in American football.</li>
<li>Confirms previous results in social science literature.</li>
<li>FOOTBALL Dataset available for anyone to use.</li>
</ul>
</section>
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