2013Q3 Reports: Program Chairs - Revision history

Poesio: /* Submissions */

2013-07-13T13:38:13Z

Submissions

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	number of short paper submissions is the highest ever - almost twice the		number of short paper submissions is the highest ever - almost twice the
	369 of last year, and 20% higher than in 2011.) These submissions were		369 of last year, and 20% higher than in 2011.) These submissions were
	managed by a program committee of 45 area chairs assisted by ~~XXX~~		managed by a program committee of 45 area chairs assisted by 711
	reviewers.		reviewers.

Poesio: /* Other recommendations */

2013-07-13T13:28:33Z

Other recommendations

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	Based on our experience this year, we would like to make the following		Based on our experience this year, we would like to make the following
	recommendations to improving the Softconf system and the reviewing process:		recommendations to improving the Softconf system and the reviewing process:
	1) Program chairs need to be aware of the physical constraints of the		# Program chairs need to be aware of the physical constraints of the conference venue before deciding on the total number of papers accepted. Conversely, the PC needs to be aware of the fact that ACL will continue to grow and needs to choose venues that would support such a large program.
	conference venue before deciding on the total number of papers accepted.		# We recommend automating more parts of the START system to allow reviewing consistency from one year to the next. Reminder emails need to be sent out automatically to area chairs and reviewers.
	Conversely, the PC needs to be aware of the fact that ACL will continue		# Following 2, we recommending providing a template of pre-defined messages for area chairs to send to reviewers.
	to grow and needs to choose venues that would support such a large program.		# Area chairs need to be given a list of reviewer emails for them to send mass emails to. This year, only program chairs can send messages directly to reviewers even though we asked the area chairs to provide instructions to the reviewers.
	2) We recommend automating more parts of the START system to allow		# Again for the sake of reviewing consistency, we should automatically keep the list of reviewers by area and add to it any new authors of published ACL papers. This list of reviewers, along with their email contact, the areas for which they reviewed previous, should be passed to the next program chairs and all area chairs to allow them to select reviewers.
	reviewing consistency from one year to the next. Reminder emails need to
	be sent out automatically to area chairs and reviewers.
	3) Following 2, we recommending providing a template of pre-defined
	messages for area chairs to send to reviewers.
	4) Area chairs need to be given a list of reviewer emails for them to
	send mass emails to. This year, only program chairs can send messages
	directly to reviewers even though we asked the area chairs to provide
	instructions to the reviewers.
	5) Again for the sake of reviewing consistency, we should automatically
	keep the list of reviewers by area and add to it any new authors of
	published ACL papers. This list of reviewers, along with their email
	contact, the areas for which they reviewed previous, should be passed to
	the next program chairs and all area chairs to allow them to select
	reviewers.

Poesio: /* Program Chairs (Pascale Fung and Massimo Poesio) */

2013-07-13T13:24:54Z

Program Chairs (Pascale Fung and Massimo Poesio)

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-===New Practices===
+===Innovations===
 This year's ACL introduces a number of innovations, the main among of which is
@@ Line 66: / Line 66: @@
 coming up with a first version of the program, which then had to be
 substantially revised.)
 ===Areas and Area Statistics===
@@ Line 331: / Line 352: @@
 Some of the traditional areas also appear to be losing in popularity, or at least researchers in those areas  may not choose to publish at ACL (see, e.g., Information Retrieval and Speech Processing).
 Note also the great disparity in acceptance rate, ranging from the 9.5% of Phonology / Morphology to the 57.1% of NLP and Creativity - so that for instance Text Mining, top single area for submissions, is only eight in terms of accepted papers.

Poesio: /* Areas and Area Statistics */

2013-07-13T13:20:34Z

Areas and Area Statistics

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	\|}		\|}

	~~Particularly popular~~ areas this year ~~included~~ Machine Translation, ~~Semantics~~, Sentiment Analysis, and ~~Text Mining~~. Some of the new areas were successful, others less so.		The top 5 areas for submissions this year were Machine Translation (when considering the two sub-areas together), Text Mining (top individual area), Sentiment Analysis, NLP Applications, and ex-aequo Semantics and Syntax/Parsing.
	The new area 'Evaluation Methods', with 25 submissions, proved reasonably popular.		Some of the new areas were successful, others less so.
	But having separate areas for 'Low Resource Language Processing' and 'NLP for the Languages of Central and Eastern Europe and the Balkans' ~~, done to incentivise submissions among NLP researchers in those areas, wasn~~'t ~~successful~~.		The new area 'Evaluation Methods', with 26 submissions (2% of the total), proved reasonably popular.
	~~It's clear that some~~ of the traditional areas ~~are~~ losing in popularity: e.g., Information Retrieval, Speech Processing, or Phonology / Morphology.		But having separate areas for 'Low Resource Language Processing' and 'NLP for the Languages of Central and Eastern Europe and the Balkans' didn't work so well; in the future it may be sensible to have a single area for Low Resource Languages.
			Some of the traditional areas also appear to be losing in popularity, or at least researchers in those areas may not choose to publish at ACL (see, e.g., Information Retrieval and Speech Processing).
	~~In addition~~ to the ~~normal presentations there will be a number~~ of ~~presentations by invited speakers and winners of awards. We are delighted to have three excellent speakers. Prof. Baayen from Tuebingen~~ and ~~Alberta has a well~~-~~deserved reputation~~ for ~~deploying innovative statistical methods in the study of language~~, ~~in particular in morphology. Chantel Prat ... In addition, we are quite pleased to have been contacted by Facebook~~, ~~who~~ is ~~expanding its NLP activities and has been recruiting a number~~ of well-known NLP researchers, contacted us offering to give a talk. Lars .... wll give a talk on .... We think that ACL should consider introducing an 'Industry Speaker' slot as a regular feature at ACL. In addition to the three invited talks we will have two additional lectures: by the winner of the best paper award and the winner of the ACL Career Award.		Note also the great disparity in acceptance rate, ranging from the 9.5% of Phonology / Morphology to the 57.1% of NLP and Creativity - so that for instance Text Mining, top single area for submissions, is only eight in terms of accepted papers.

Poesio: /* Areas and Area Statistics */

2013-07-13T13:05:20Z

Areas and Area Statistics

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	{\| class="wikitable" cellpadding="2" border="1"		{\| class="wikitable" cellpadding="2" border="1"
	\|Areas \|\|Long Received\|\|Long Accepted\|\|Short Received\|\|Short accepted\|\|Total (and % of total ~~submissions~~)\|\|Total (and % of total ~~accepts~~)\|\|Area accept rate		\|Areas \|\|Long Received\|\|Long Accepted\|\|Short Received\|\|Short accepted\|\|Total submissions (and % of total)\|\|Total accepts (and % of total)\|\|Area accept rate
	\|-		\|-
	\|Cognitive Modelling and Psycholinguistics\|\|13 \|\|3 \|\|12 \|\|5 \|\| 27 (2% ) \|\| 8 (2.4%)\|\|29.6%		\|Cognitive Modelling and Psycholinguistics\|\|13 \|\|3 \|\|12 \|\|5 \|\| 27 (2% ) \|\| 8 (2.4%)\|\|29.6%

Poesio: /* Areas and Area Statistics */

2013-07-13T13:04:08Z

Areas and Area Statistics

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	\|-		\|-
	\|Syntax and Parsing		\|Syntax and Parsing
	\|		\|39
	\|		\|13
	\|		\|43
	\|		\|13
			\|82 (6.4%)
			\|26 (7.9%)
			\|31.7%
	\|-		\|-
	\|Tagging and Chunking		\|Tagging and Chunking
	\|		\|12
	\|		\|2
	\|		\|13
	\|		\|2
			\|25 (1.9%)
			\|4 (1.2%)
			\|16%
	\|-		\|-
	\|Text Mining and Information Extraction		\|Text Mining and Information Extraction
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	\|53		\|53
	\|9		\|9
			\|121 (9.4%)
			\|16 (4.9%)
			\|13.2%
	\|-		\|-
	\|Word Segmentation		\|Word Segmentation
	\|		\|13
	\|		\|2
	\|		\|15
	\|		\|4
			\|28 (2.2%)
			\|6 (1.8%)
			\|21.4%
	\|-		\|-
	\|~~TOTAL~~		\|TOTALS
	\|662		\|662
	\|174		\|174

Poesio: /* Areas and Area Statistics */

2013-07-13T12:56:17Z

Areas and Area Statistics

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	{\| class="wikitable" cellpadding="2" border="1"		{\| class="wikitable" cellpadding="2" border="1"
	\|Areas \|\|Long Received\|\|Long Accepted\|\|Short Received\|\|Short accepted\|\|Total (and % of total submissions)\|\|Total (and % of total accepts)\|\|		\|Areas \|\|Long Received\|\|Long Accepted\|\|Short Received\|\|Short accepted\|\|Total (and % of total submissions)\|\|Total (and % of total accepts)\|\|Area accept rate
	\|-		\|-
	\|Cognitive Modelling and Psycholinguistics\|\|13 \|\|3 \|\|12 \|\|5 \|\| 27 (2% ) \|\| 8 (2.4%)		\|Cognitive Modelling and Psycholinguistics\|\|13 \|\|3 \|\|12 \|\|5 \|\| 27 (2% ) \|\| 8 (2.4%)\|\|29.6%
	\|-		\|-
	\|Dialogue and Interactive Systems		\|Dialogue and Interactive Systems
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	\|25 (1.9%)		\|25 (1.9%)
	\|5 (1.5%)		\|5 (1.5%)
			\|20%
	\|-		\|-
	\|Discourse, Coreference, and Pragmatics		\|Discourse, Coreference, and Pragmatics
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	\|47 (3.6%)		\|47 (3.6%)
	\|13 (4%)		\|13 (4%)
			\|27.6%
	\|-		\|-
	\|Evaluation Methods		\|Evaluation Methods
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	\|26 (2%)		\|26 (2%)
	\|7 (2.1%)		\|7 (2.1%)
			\|26.9%
	\|-		\|-
	\|Information Retrieval		\|Information Retrieval
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	\|40 (3.1%)		\|40 (3.1%)
	\|5 (1.5%)		\|5 (1.5%)
			\|12.5%
	\|-		\|-
	\|Language Resources		\|Language Resources
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	\|51 (4%)		\|51 (4%)
	\|14 (4.2%)		\|14 (4.2%)
			\|27.4%
	\|-		\|-
	\|Lexical Semantics and Ontologies		\|Lexical Semantics and Ontologies
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	\|50 (3.9%)		\|50 (3.9%)
	\|9 (2.7%)		\|9 (2.7%)
			\|18%
	\|-		\|-
	\|Low Resource Language Processing		\|Low Resource Language Processing
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	\|23 (1.8%)		\|23 (1.8%)
	\|8 (2.4%)		\|8 (2.4%)
			\|34.8%
	\|-		\|-
	\|Machine Translation: Methods, Applications and Evaluations		\|Machine Translation: Methods, Applications and Evaluations
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	\|108 (8.4%)		\|108 (8.4%)
	\|25 (7.6%)		\|25 (7.6%)
			\|23.1%
	\|-		\|-
	\|Machine Translation: Statistical Models		\|Machine Translation: Statistical Models
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	\|74 (5.7%)		\|74 (5.7%)
	\|31 (9.4%)		\|31 (9.4%)
			\|41.9%
	\|-		\|-
	\|Multilinguality		\|Multilinguality
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	\|21 (1.6%)		\|21 (1.6%)
	\|8 (2.4%)		\|8 (2.4%)
			\|38%
	\|-		\|-
	\|NLP Applications		\|NLP Applications
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	\|88 (6.9%)		\|88 (6.9%)
	\|25 (7.6%)		\|25 (7.6%)
			\|28.4%
	\|-		\|-
	\|NLP and Creativity		\|NLP and Creativity
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	\|7 (0.5%)		\|7 (0.5%)
	\|4 (1.2%)		\|4 (1.2%)
			\|57.1%
	\|-		\|-
	\|NLP for the Languages of Central and Eastern Europe and The Balkans		\|NLP for the Languages of Central and Eastern Europe and The Balkans
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	\|10 (0.8%)		\|10 (0.8%)
	\|4 (1.2%)		\|4 (1.2%)
			\|40%
	\|-		\|-
	\|NLP for the Web and Social Media		\|NLP for the Web and Social Media
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	\|58 (4.5%)		\|58 (4.5%)
	\|10 (3%)		\|10 (3%)
			\|17.2%
	\|-		\|-
	\|Phonology and Morphology		\|Phonology and Morphology
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	\|21 (1.6%)		\|21 (1.6%)
	\|2 (0.6%)		\|2 (0.6%)
			\|9.5%
	\|-		\|-
	\|Question Answering		\|Question Answering
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	\|26 (2%)		\|26 (2%)
	\|7 (2.1%)		\|7 (2.1%)
			\|26.9%
	\|-		\|-
	\|Semantics		\|Semantics
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	\|32		\|32
	\|10		\|10
			\|82 (6.4%)
			\|27 (8.2%)
			\|32.9%
	\|-		\|-
	\|Sentiment Analysis, Opinion Mining and Text Classification		\|Sentiment Analysis, Opinion Mining and Text Classification
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	\|60		\|60
	\|15		\|15
			\|106 (8.2%)
			\|27 (8.2%)
			\|25.4%
	\|-		\|-
	\|Spoken Language Processing		\|Spoken Language Processing
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	\|12		\|12
	\|2		\|2
			\|21 (0.2%)
			\|11 (0.3%)
			\|52.3%
	\|-		\|-
	\|Statistical and Machine Learning Methods in NLP		\|Statistical and Machine Learning Methods in NLP
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	\|32		\|32
	\|5		\|5
			\|67 (5.2%)
			\|16 (4.9%)
			\|23.9%
	\|-		\|-
	\|Summarization and Generation		\|Summarization and Generation
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	\|23		\|23
	\|6		\|6
			\|54 (4.2%)
			\|17 (5.2%)
			\|31.5%
	\|-		\|-
	\|Syntax and Parsing		\|Syntax and Parsing

Poesio: /* Areas and Area Statistics */

2013-07-13T12:41:05Z

Areas and Area Statistics

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	{\| class="wikitable" cellpadding="2" border="1"		{\| class="wikitable" cellpadding="2" border="1"
	\|Areas \|\|Long Received\|\|Long Accepted\|\|Short Received\|\|Short accepted\|\|Total (% of total submissions)\|\|Total and % of total ~~acceptances~~\|\|		\|Areas \|\|Long Received\|\|Long Accepted\|\|Short Received\|\|Short accepted\|\|Total (and % of total submissions)\|\|Total (and % of total accepts)\|\|
	\|-		\|-
	\|Cognitive Modelling and Psycholinguistics\|\|13 \|\|3 \|\|12 \|\|5 \|\| 27 (2% ) \|\| 8 (2.4%)		\|Cognitive Modelling and Psycholinguistics\|\|13 \|\|3 \|\|12 \|\|5 \|\| 27 (2% ) \|\| 8 (2.4%)
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	\|-		\|-
	\|Machine Translation: Statistical Models		\|Machine Translation: Statistical Models
	\|		\|44
	\|		\|21
	\|		\|30
	\|		\|10
	\|		\|74 (5.7%)
	\|		\|31 (9.4%)
	\|-		\|-
	\|Multilinguality		\|Multilinguality
	\|		\|11
	\|		\|3
	\|		\|10
	\|		\|5
	\|		\|21 (1.6%)
	\|		\|8 (2.4%)
	\|-		\|-
	\|NLP Applications		\|NLP Applications
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	\|42		\|42
	\|15		\|15
			\|88 (6.9%)
			\|25 (7.6%)
	\|-		\|-
	\|NLP and Creativity		\|NLP and Creativity
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	\|4		\|4
	\|2		\|2
			\|7 (0.5%)
			\|4 (1.2%)
	\|-		\|-
	\|NLP for the Languages of Central and Eastern Europe and The Balkans		\|NLP for the Languages of Central and Eastern Europe and The Balkans
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	\|8		\|8
	\|3		\|3
			\|10 (0.8%)
			\|4 (1.2%)
	\|-		\|-
	\|NLP for the Web and Social Media		\|NLP for the Web and Social Media
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	\|26		\|26
	\|2		\|2
			\|58 (4.5%)
			\|10 (3%)
	\|-		\|-
	\|Phonology and Morphology		\|Phonology and Morphology
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	\|10		\|10
	\|0		\|0
			\|21 (1.6%)
			\|2 (0.6%)
	\|-		\|-
	\|Question Answering		\|Question Answering
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	\|12		\|12
	\|3		\|3
			\|26 (2%)
			\|7 (2.1%)
	\|-		\|-
	\|Semantics		\|Semantics

Poesio: /* Areas and Area Statistics */

2013-07-13T12:31:04Z

Areas and Area Statistics

← Older revision		Revision as of 12:31, 13 July 2013
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	\|-		\|-
	\|Low Resource Language Processing		\|Low Resource Language Processing
	\|		\|10
	\|		\|3
	\|		\|13
	\|		\|5
			\|23 (1.8%)
			\|8 (2.4%)
	\|-		\|-
	\|Machine Translation: Methods, Applications and Evaluations		\|Machine Translation: Methods, Applications and Evaluations
			\|50
			\|11
			\|58
			\|14
			\|108 (8.4%)
			\|25 (7.6%)
			\|-
			\|Machine Translation: Statistical Models
	\|		\|
	\|		\|
	\|		\|
	\|		\|
	\|-
	~~\|Machine Translation: Statistical Models~~
	\|		\|
	\|		\|
			\|-
			\|Multilinguality
	\|		\|
	\|		\|
	\|-
	~~\|Multilinguality~~
	\|		\|
	\|		\|

Poesio at 12:25, 13 July 2013

2013-07-13T12:25:36Z

← Older revision		Revision as of 12:25, 13 July 2013
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	===Presentations===		===Presentations===
	174 (26%) long papers and 154 (24%) short papers were selected for		174 (26%) long papers and 154 (24%) short papers were selected for
	presentation in the conference. Among the accepted papers, 111 of the		presentation in the conference (12 long paper submissions and 35 short paper submissions were either
			withdrawn or rejected without review for violating anonymity or page length rules).
			Among the accepted papers, 111 of the
	long papers will have an oral presentation (in 37 sessions of 3 papers		long papers will have an oral presentation (in 37 sessions of 3 papers
	each) as well as 56 of the short papers (in 14 sessions of 4 papers		each) as well as 56 of the short papers (in 14 sessions of 4 papers
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	\|-		\|-
	\|Information Retrieval		\|Information Retrieval
	\|		\|20
	\|		\|1
	\|		\|20
	\|		\|4
			\|40 (3.1%)
			\|5 (1.5%)
	\|-		\|-
	\|Language Resources		\|Language Resources
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	\|27		\|27
	\|5		\|5
			\|51 (4%)
			\|14 (4.2%)
	\|-		\|-
	\|Lexical Semantics and Ontologies		\|Lexical Semantics and Ontologies
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	\|25		\|25
	\|6		\|6
			\|50 (3.9%)
			\|9 (2.7%)
	\|-		\|-
	\|Low Resource Language Processing		\|Low Resource Language Processing